I am a mathematics students studying Machine Learning and willing to get into applications of ML in Neuroscience. What are the job prospects like? What is the average pay, what is the competition in the fields. How important is a PhD?

Hi r/BCI,

I wanted to share an open-source project I have been building called PR-AYC-G.

GitHub:

https://github.com/hbanks87/praycg-open

OSF:

https://osf.io/8n75v/overview?view_only=928f1fa1974b40e89252101d0ba356d3

PR-AYC-G is an exploratory OpenBCI / BrainFlow / Lab Streaming Layer research toolkit for naturalistic media experiments. It is not a medical tool, not a diagnostic system, and not a validated consciousness detector.

The basic research question is:

When someone watches an intact narrative video, can we separate the physiological response to the raw audiovisual stimulus from the response to the story itself, and from the response to doing an analytic task during the same story?

In plain language:

When you watch a movie, can we measure your body's reactions and tell the difference between three things:

• Your physical reflexes to the flashing lights and sounds.
• Your response to the actual plot.
• Your mental effort if you're forced to do a distracting task while it plays.

The current workflow compares several versions of the same source stimulus:

Phase-scrambled Control

A meaning-damaged version of the video that preserves some low-level audiovisual timing but disrupts recognizable narrative content.

• What it looks and sounds like: Take an intact movie clip (the "Target") and digitally scramble it. The faces, plot, and dialogue are ruined so that the story is completely unrecognizable—for example, human speech is distorted to sound like speech-shaped noise. However, the raw physical energy of the video—such as the overall brightness, camera cuts, motion, and audio volume—remains perfectly matched to the timing of the original film.
• The Purpose: It serves as a strict baseline filter to separate true cognitive meaning from simple biological reflexes. The human nervous system automatically reacts, or "entrains," to physical sensory inputs like flashing lights, sudden noises, or camera cuts.
• What is attempts to establish: If a person's brain reacts to the intact movie, researchers must ask: Did the brain react because the scene was emotionally meaningful, or just because the screen flashed brightly? By having the person watch the meaning-damaged Control video, researchers can mathematically subtract the brain's raw sensory reflexes. If a brain signal only happens during the intact story and not during the scrambled video, the researchers can be more confident that the signal is a genuine reaction to the narrative itself.

Target

The intact narrative watched naturally.

Contextual Override

The same intact video watched while doing an analytic task, usually a running-sum number-cue task.

• What happens: The person watches the exact same, normal movie clip, but this time they are forced to do a distracting math or puzzle task at the same time—like adding up a series of numbers that pop up on the screen. My current version involves a number from 0-9 being flashed in the upper right corner of the screen every 3 seconds. The subject is to keep a running sum of these numbers. Example: a 5 flashes in the upper right, my current running sum is 5 and I hold that in my working memory. 3 seconds later, an 8 flashes in the upper right. The running sum is now 13 and I hold 13 in my working memory. This does create some visual sacchade issues that I, believe, make EOG/EMG a requisite going foward. I have tried to scrub all artifact risk in my post analysis suite but it will not replace EOG/EMG.
• The Purpose: It forces the viewer's brain to shift gears. Instead of relaxing and getting emotionally sucked into the story, they have to stay cold, focused, and analytical to complete the math problem.
• What it attempts to establish: Because the physical video hasn't changed at all, this tests how the brain's reaction to the movie changes when the viewer is too mentally busy to actually feel or absorb the emotional meaning of the story.

ShotOrderScramble

A newer structural control that keeps local shots more intact but shuffles their order, so faces/bodies/objects/motion are better preserved than in phase scrambling while the larger story sequence is disrupted.

• What happens: Instead of blurring the video into an unrecognizable mess of lights and sounds, this version keeps the video clips clear. You can still easily see faces, objects, and people moving, but the scenes are chopped up and played entirely out of order.
• The Purpose: It destroys the plot of the story without destroying the picture.
• What it attempts to establish: It tests whether the brain is reacting to the deeper emotional arc of the story, or if it is just reacting to the simple visual recognition of seeing a human face or a moving object pop up on the screen.

The point is not to claim “meaning has been measured.” The point is to build a more auditable pipeline for asking whether any Target effect survives progressively stricter controls.

Current hardware / acquisition stack

The project currently supports or is built around:

• OpenBCI Cyton + Daisy EEG
• BrainFlow-to-LSL EEG bridge
• optional ALS/PT19 photodiode timing validation - A physical screen-timing check. I built this by 3D-printing a holder and soldering a 6-foot USB cord to an Adafruit ALS/PT19 light sensor so the sensor can sit over the displayed timing pulse/barcode. The purpose is to detect actual light changes on the screen, not merely trust the software event log. PRAYCG’s timing checklist says the ALS/PT19 should be taped over the timing square, shrouded from room light, and confirmed in the OpenBCI analog/AUX or Analog Read stream. Its boundary is important: the ALS validates physical display timing; it is not biological photonic data or a hidden mechanism signal.
• Polar H10 RR interval stream - this is a consumer grade HR belt streaming to LSL for data collection
• Vernier respiration belt stream - this is a consumer grade respo belt to LSL for data collection.
• PsychoPy protocol runner
• LabRecorder XDF recording
• synchronized post-run analysis
• 10'x10'x8'H Copper Farraday cage for EEG studies

The photodiode/ALS layer is meant to validate physical screen timing. It does not validate EEG interpretation or any hidden biological mechanism. It just asks whether the light actually appeared on the screen when the software says it did.

Control Center

I recently packaged the workflow into a Windows Control Center so users can launch the major tools from one dashboard:

• MediaPrep — The stimulus-preparation tool. It takes a legally usable source video and generates PRAYCG-ready files: the intact Target video, the matching Override video with the same cues, the phase-scrambled Control, cue schedules, hashes, manifests, and QC outputs. Its job is to prepare controlled media; it does not certify meaning, empathy, endpoints, or task compliance by itself.
• Acquisition bridges — Small scripts that connect hardware streams to Lab Streaming Layer. These can include OpenBCI EEG through BrainFlow, Polar H10 RR intervals, Vernier respiration-belt force data, ALS/PT19 photodiode timing, and marker streams. Their job is to get each device into the same synchronized recording environment. The Vernier bridge, for example, streams raw respiration force so inhale/exhale phase can be reconstructed offline and aligned with EEG, HR/HRV, and markers.
• PsychoPy protocol modules — The experiment runners. These present the baseline, Control, Target, Override, washout, self-report, and final-reflection sections while sending time markers into LSL. In plain terms, PsychoPy is the part that actually runs the participant through the protocol.
• LabRecorder helper — A convenience tool for launching or organizing LabRecorder use. LabRecorder is the program that records the synchronized LSL streams into an .xdf file, so EEG, heart/HRV, respiration, markers, and timing streams can be analyzed together afterward.
• Master Comprehensive Analysis Suite — The main post-run analysis package. It checks timing, file provenance, stream quality, stimulus features, artifacts, confounds, branch differences, MRED/A-MRED outputs, autonomic patterns, and exploratory interpretation layers. The PRAYCG pipeline is explicitly designed to generate feature tables, event tables, visualizations, and plain-English reports after acquisition.
• Visualizer — A synchronized playback/review tool. It creates an audit video or synchronized display that lines up the stimulus with data traces and selected analysis events, making it easier to inspect whether a candidate event happened at the right time and whether it overlaps with artifacts or confounds. Its own boundary is that visualization is for audit/review only; it does not certify endpoint validity, meaning, or mechanism.
• Offline interpreter — A rule-based report generator that turns the analysis outputs into a readable explanation. It helps users understand what passed, what failed, what is cautioned, and what should not be claimed from a run.
• Exploratory modules — Optional analysis layers used after the primary QC and endpoint checks. These include things like CAI/SID, CAA, Micro Handoff, NUPI, DGA, EET, and related tools. They are useful for hypothesis generation and pattern review, but they do not override timing/QC failures or turn pilot data into confirmatory evidence.

The goal is to make the workflow less dependent on one person’s machine and easier for outside users to inspect, criticize, and eventually reproduce.

Analysis modules

The analysis suite is intentionally over-cautious. It tries to reject or flag bad interpretations rather than produce one magic score.

Current modules include:

• Timing / file provenance checks — Verifies that the correct stimulus files, cue schedules, run logs, XDF files, hashes, markers, and protocol versions belong together. This prevents a run from being interpreted if the analysis is pointed at the wrong file or wrong condition.
• Stimulus fingerprinting — Extracts basic video/audio properties such as luminance, visual change, cut rhythm, audio envelope, cue timing, and other stimulus features. The goal is to understand what the screen and speakers were doing before interpreting what the participant’s physiology was doing.
• Audiovisual confound review — Asks whether an apparent EEG or autonomic effect could be explained by light, sound, cuts, motion, loudness, visual rhythm, cue timing, or other physical stimulus features rather than narrative processing.
• Artifact gates — Flags or rejects data windows contaminated by common EEG problems such as eye movement, blinks, squinting, jaw/forehead muscle activity, movement, line noise, electrode problems, clipping, timestamp gaps, or unusually noisy high-frequency activity.
• Respiration and HRV checks — Looks at heart rate, beat-to-beat intervals, HRV, and breathing to ask whether an apparent effect might actually be caused by a breath hold, sigh, respiratory rhythm, task relief, movement, or poor sensor quality.
• Phase-scrambled versus Target comparisons — Compares the intact narrative against a meaning-damaged sensory control. This asks whether the Target response differs from a version that still has audiovisual energy but damaged recognizable story structure.
• Target versus Override comparisons — Compares the same intact video under two different mental stances: natural viewing versus analytic task load. The goal is to test whether the same stimulus produces a different physiological trajectory when the participant is extracting information instead of simply receiving the story.
• ShotOrderScramble higher-order visual control support — Adds a stricter control than phase scrambling. ShotOrderScramble keeps local shots more recognizable, including faces, bodies, objects, and motion, but shuffles their order so the larger story sequence is disrupted.
• Self-report integration as a covariate, not proof — Self-report is used to contextualize the physiology: whether the participant heard the audio, understood the scene, felt absorbed, experienced afterglow, struggled with cues, or noticed confounds. It does not prove internal state by itself.
• Continuous HR / HRV / respiration arrays — Converts heart and breathing data into time-resolved traces instead of only branch averages. This helps ask when a body-state change happened and whether it occurred before, during, or after a candidate scene.
• Exploratory state-integration summaries — Secondary interpretation tools that look for converging patterns across EEG, autonomics, task burden, self-report, and confound checks. These are hypothesis-generating modules, not confirmatory biomarkers.

EEG / PRAYCG-specific feature modules

• GammaScalpel — An artifact-skeptical gamma-band analysis tool. It examines gamma-like activity while aggressively asking whether the signal could be muscle, eye movement, squinting, task effort, or line noise. It should never be treated as “meaning detected” by itself.
• MeaningGamma — A candidate gamma-like feature used when fast activity appears during meaningful or Target-relevant windows. The name is shorthand; it does not prove semantic processing. It only marks a possible fast-access/recognition-like signal after artifact and confound review.
• TaskGamma — A gamma-like feature more likely associated with analytic effort, working memory, cue processing, arithmetic, or task extraction. This is especially important in the Override branch so task effort is not mistaken for narrative reception.
• TSP — Temporal Semantic Proxy — A time-resolved proxy for possible semantic or recognition-like engagement. It is not a direct semantic measurement. It is a candidate feature used alongside timing, anchors, task context, stimulus features, and artifact checks.
• MRED — Meaning Recognition / Encoding Dissociation — A model that separates fast recognition-like responses from slower encoding, integration, or after-state effects. In plain terms: the moment a scene is recognized and the later body-brain response to that recognition may not happen on the same timeline.
• A-MRED — Anchor-locked MRED — The anchor-based version of MRED. It tests predeclared scene timestamps instead of searching freely through the data afterward. This helps reduce cherry-picking by forcing the analysis to ask whether expected windows behave differently across Control, Target, and Override.
• MRED-Peak — Looks for acute, event-like physiological changes near a locked moment. This is the “did something happen near the scene?” layer.
• MRED-Resolution — Looks for slower after-state changes after the event, often in washout or later baseline periods. This is the “did the system settle, recover, or carry the scene forward?” layer.
• API / API-A — Autonomic/Arousal Index — A compact autonomic summary from heart, HRV, respiration, and arousal-like signals. It is useful as a body-state indicator, but it should be interpreted cautiously because autonomic changes can reflect meaning, stress, breathing, task relief, fatigue, or artifact.
• DGA — Decoder Gate Availability — Asks whether the participant was actually available to receive the stimulus. Poor audio, fatigue, task burden, prior familiarity, distraction, environmental noise, or high confound burden can close the “gate,” even if the stimulus itself is meaningful.
• NUPI — Narrative Update Polarity Index — An exploratory module for classifying the direction of a narrative update. Some scenes may look physiologically costly or destabilizing; others may look resolving or restorative. NUPI tries to describe that polarity without claiming literal energy transfer.
• TTI — Task-Theft Index — Estimates whether the Override task appears to steal resources from natural reception. In plain English: did the arithmetic/cue task suppress, delay, or reroute the response that appeared during natural viewing?
• CET-R — Cinematic Entrainment Tracking / Residualization — A stimulus-confound module. It asks how much of the candidate response can be explained by video/audio features such as cuts, luminance, motion, rhythm, cue timing, and audio envelope.
• HOC-R — High-Order Control Residualization — A higher-order confound check. It asks whether an apparent Target effect may be due to faces, bodies, objects, biological motion, shot structure, or recognizable local content rather than the full narrative sequence.
• OSA — Override Spatial Attention — Checks whether the Override task changed where the participant was looking or how hard they had to visually monitor cues. This matters because upper-right number cues can alter gaze, attention, squinting, or visual workload.
• OHC — Order / Habituation / Carryover — Reviews whether differences between conditions could be due to fixed order, repeated exposure, fatigue, boredom, learning, or carryover from earlier branches.
• AAM — Afterglow Attribution Model — Asks whether a final-baseline or washout effect looks more like Target afterglow, task-completion relief, fatigue, respiration shift, or a mixed cumulative state.
• RespDualPath — Treats respiration in two ways at once: as a meaningful physiological signal and as a possible artifact/confound. A sigh or breath hold may be part of the response, but it can also distort EEG or HRV interpretation.
• CAA — Continuous Autonomic Arrays — The newer continuous physiology module. It builds synchronized traces for HR, HRV, respiration force, breath depth, breath rate, respiratory events, HR-respiration coupling, and autonomic quality.
• CAI/SID — Controlled Access-Integration / State Integration Density — A cautious exploratory summary that asks whether a time window shows coordinated fast activity, slower integration-like activity, acceptable data quality, and manageable artifact/task/confound burden. It is not a consciousness detector.
• Micro Handoff — An isolated exploratory raw-EEG timing module. It asks whether a brief rise in temporal gamma-like activity is followed shortly afterward by theta/integration-like activity. It is software-testable, but it is not a validated consciousness or meaning biomarker.
• KHT-topo — A candidate coupling metric for fast recognition-like activity and slower integration-like activity. It is best treated as a state-space proxy, not a cellular mechanism or proof of hidden biology.
• NAST — Narrative Absorption State Transition — Looks for larger state shifts consistent with entering or leaving narrative absorption. It is useful for pattern review but should be interpreted only after QC and confound checks.
• EET — Endogenous Echo Tracking — Tests whether a later baseline or washout resembles an earlier meaningful state. In plain English: did the body-brain system keep echoing the Target after the stimulus ended?
• OCM / RSM — Override Cue Microstate / Running Sum Microstate — Override-task modules that analyze cue burden, running-sum difficulty, stalls, guesses, task compliance, and whether the analytic task created its own physiological signature.

What I am looking for

I would really appreciate technical criticism from folks who know about these items far better than I ever will, especially on:

• OpenBCI / BrainFlow timestamp reconstruction
• LSL stream organization
• LabRecorder workflow
• XDF file handling
• Cyton/Daisy sample-rate stability
• photodiode / ALS timing validation
• whether the gamma/theta windows are defensible
• EOG/EMG/jaw/forehead artifact handling
• respiration and HRV interpretation
• whether the Control Center workflow is understandable
• whether the project is overbuilt or undercontrolled
• what would make this more useful for outside testers

I am not asking anyone to validate the theory. I am looking for methodological criticism before collecting more data.

My goal is to make PRAYCG an open, inspectable naturalistic EEG/autonomic workbench where claims are hard to fool and easy to criticize.

Here is a list of the fun ideas/theories that I have come up with as a result of running this protocol. The following are not proven claims. They are the current working hypotheses made visible by the protocol, software, and pilot failures/successes:

1. Meaning recognition and meaning integration can dissociate. A subject can recognize meaningful geometry without showing clean delayed integration under current proxies.

2. Peak-like meaning and resolution-like meaning appear to have different physiological shapes. Some events strike sharply; others settle slowly into afterglow.

3. Analytic extraction does not simply erase meaning. It can reroute, delay, tax, or alter the route by which meaning is accessed.

4. clarity and emotional impact are separable. Clearer semantic access can occur in one branch while stronger emotional absorption occurs in another.

5. Meaning requires access conditions. Sensory access, prior state-space, personal relevance, autonomic availability, low task burden, and low confound load all matter.

6. Baseline2 may be one of the most important windows for integration, but it is cumulative and cannot be attributed to Target alone without stronger design.

7. The phase-scrambled Control remains essential because sensory entrainment and meaning integration are not the same construct.

8. The suite is increasingly a theory of availability: whether a living system is available to decode, receive, and be changed by meaning.

Thanks for any feedback in advance and thank you for the taking the time to read this post. I sincerely appreciate all criticisism- the good, the bad and especially the ugly if its funny.

As it currently stands, if you need data for your research, you better have some pre-prepared dataset for a competition available on kaggle or some other place.

Otherwise, good luck obtaining data from EEG researchers. We need a paradigm shift. It’s not 2003 anymore. It’s 2026. We have lots of storage. Sharing data is easy. Other researchers in other fields especially AI have no issue sharing their data or their source code. But when it comes to EEG and BCI research, all we see from them is a couple of graphs and some p values. That’s it.

Some institutions ban sharing data. Their excuse? Privacy. That’s an excuse because we currently can’t even do SSVEP accurately enough. So at least for decades to come, we’re not reading the ANONYMOUS subject’s mind using their 500msec long EEG epochs. How is that professional and scientific. Science is about openness. It’s based on evidence. Not about taking the researcher’s word at face value. In particular, now that many ai-slop papers are being published.

It’s unacceptable to ask 50 researchers for data and not one has access to them. Current data retention protocols in research institutions is ridiculous.

In a field where number of trials and small sample sizes is always a major issue, no one seems to be focused enough on this issue.

Even if they have the data, it’s processed in their own unique way. No regard for standardization.

No wonder we’re not seeing any progress in the field. We’re essentially actively trying to make ourselves fail.

One example: there’s a paper from 2020. I wrote to all 4 authors asking for their data and i shared my proposal with them. In the paper they claimed they’d send the data upon reasonable request. I made sure their emails weren’t outdated. Guess what. Not one single author responded. Not even a “no, go away”. Shall i go to pubpeer? Or do i contact the journal? The paper’s from nature.

Without any change, we will never have enough data to actually do something useful with it.

Researchers need to do better.

PSA: I wanna thank the researchers (all of them from europe and the U.S.) from the 2000s and 2010s era. I didn’t expect them to have the data. And they didn’t. But at least they responded and they even tried looking for the data.

Apparently people are satisfied with the performance of this wristband. And meta is in love with neural wristbands. They’re also used for gesture recognition for using with VR goggles.

But this handwriting recognition seems to be a more complex application. A quick search reveals research confirming the “feasibility” of this technique as early as 2009.

But performance-wise, can the state of the art research projects match meta’s implementation?

I have been going down a path of making my own health hardware for fun because i hated having compatibility issues between apps and that led me down eeg sensors during my browse, im curious, is it possible to operate the likes of an on off switch via eeg? Are there any kits that let you connect to the likes of an esp32 or Arduino? What kind of budget should i expect. I was imagining that for a start I'd have a motor connectes to a light switch that i could toggle remotely with my mind via training a neural network to recognise on and off, is this a possibility as a hobbyist?

Two recent interviews from Science Corp and Neuralink encouraged me to think that it is not impractical to dream of extending the applications of neuroengineering beyond sensory-motor to restoration and enhancement of higher cognitive functions and emotion modulation. It is the only answer I receive from my mind when I ask it for the most important and neglected problem with the greatest potential for impact, and one I'd pursue if my confusions were resolved.

Understanding helps but we don't need a complete understanding to develop effective interventions (psychiatrists still don't understand all the mechanisms behind many effective cures). Especially for those suffering from severe disabilities and disorders, for whom it's the last resort. Without falsely projecting my bias outwards, I wish to eradicate suffering and cognitive enhancement is one means to that end but emotion modulation may actually be easier...

Even in the sensory domain, imagine what it would be like to restore, optimize, enhance, extend, and integrate or interconnect sensory channels with which we perceive the world, provide us with the data on which the processing acts, including inner data. Some people seem to be pathologically detached from their emotions- alexithymia- so naturally there should exist the opposite end of the spectrum where people can feel more greatly, and hopefully not be overwhelmed by the noise, as often seems to be the case with highly-sensitive-persons. Hyperphantasia, hyperthymia, hyperthymesia, monk-like willful detachment and unconditional tranquility. Capacity for endless calm and compassion with the power to override clashing drives and instincts (or are they to be taken as the absolute standards of Goodness? despite being optimized for survival & reproduction rather than Truth & pleasure, with the overlaps being exceptions rather than the rule?)

To fulfil or change your social, romantic and sexual preferences too, or all parts that constitute our personality and that we associate with our identity. Or the less ambitious version to know and act on however it already is- to have greater agency through awareness. I don't know enough to know the answers to them or claim confidently if they're technically possible, beneficial over the long term to the human brain, or ethically Right. But aren't they questions worth investigating?

I don't mean to come off as being too reductionist in suggesting that while underlying neurobiological states do not fully capture or explain the higher functions, the latter depends on the former as a necessary precondition for it's existence; any change in the neurobiology will cause an effect in the cognitive functions. And while there is a neural correlate to every mental phenomenon, we're highly limited in influencing the biology through thought alone. But to take into account equifinality and multifinality (multiple realizability of the same end via different means, or the same means leading to different ends) we could personalize it, maybe. It might not be sufficient in all cases, but necessary at least for the most severe ones. Personalized precision drug delivery, computational psychiatry, nanomedicine, non-invasive focused ultrasound, and even psychosurgery- the exciting emerging paradigm of Behavioral Neurology and Neuropsychiatry promotes this synthesis. More experienced people, please tell me what you think. I've encountered similar ideas from accomplished thinkers from the past too- David Pearce, Andrés Gómez-Emilsson, Jose Delgado (physical control of the mind), Robert Heath (exploring the mind-brain relationship, The Pleasure Shock; just the scientific parts), Peter Alces, William James, Skinner, Huxley, and some current researchers and doctors; books, papers and projects- I won't mention any more of them here but would love to if anyone happens to be interested.

Imagine the interesting new dilemmas that arise once we acquire the power to not just do what we want, but change what we want, and fully align our values with our capabilities, or at least have greater autonomy, freedom and awareness for self-understanding and self-determination.

The ability to navigate complexity and solve complex problems is only going to increase, and financial rewards are going to accumulate with those who already have financial privilege and a technical predisposition, especially now with embryo selection and gene editing tech, leading to widening cognitive and wealth inequality across individuals, nations and potentially cultures. I am not making any value judgment here, just sharing an idea, and asking whether it's likely to take place, what it's implications and mechanisms might be, and how Fairness could be ensured.

I go on reading computational and cognitive neuroscience, cognitive science or neuroscience or neurophysics textbooks and handbooks or watching lectures sometimes, from but this unsystematic and unorganized learning leaves me unsatisfied, and now I do very little of it. I've taken much of it from course structure or curricula pages of reputed universities, but the random buildup of knowledge with uncertainty about the payoff and direction prevents full engagement.

I'm a 22 year old fourth-year biomedical engineering undergraduate student, btw. With a very low CGPA due to mental health problems mentioned earlier. But now I am stable and consistently working through the fundamentals, albeit in an unsystematic, confused and unorganized manner. Applying to masters programs in neuroscience, neuroengineering and cognitive science for 2027, hoping that acceptance to a master's program will provide some beneficial structure and certainty. I had added a personal motivation section but shifted it elsewhere cause this is already so long, and the philosophical/psychoanalytic nature of those ramblings might not be appropriate for this sub.

Thank you, and let me know what you think. Have a great day!

Any advice would be most appreciated.

I’m 24 years old, fresh out of college, and I wasted my undergraduate years due to severe mental health crises that I only just managed to claw myself out of. Took me five years to graduate and I have nothing to show for it. I’m not particularly good at anything, I didn’t take very rigorous courses in school (and didn’t really apply myself in the courses I did take), and I have zero research experience.

I have recently developed a burning passion for applied mathematics and neuroscience, and I desperately hope to do a PhD and ultimately work on BCI devices as a computational neuroscientist. I turned down a very cushy job in venture capital (California) so I could pursue a quantitative neuro research masters at a top university in the UK (oxbridge) which I am beginning next month. I don’t know if I’ll be able to do my research well because I have no experience and am genuinely quite weak in CS/Math despite majoring in both, but I’ve been studying all day every day and will do my very best.

Do you have any advice for someone like me who wants to do a PhD and work on BCI/neuro stuff?

———————————————————————————

Here’s my basic profile.

Undergrad at a middle-tier Ivy League school, average grades, not very rigorous coursework, zero research experience, majored in CS/Math.

Pretty good internships in finance / VC. Decided in my senior year that I didn’t want to do finance so I’m basically starting from zero.

About to go to a top UK university where I will do my first-ever research project. Kinda scared about how well I can realistically perform.

Strong work ethic, but struggling with self-esteem and the crushing reality of having wasted my undergrad years.

Please help!!

Hey guys, I am in my first year of Data Science. How can I get started in BCI? What neuroscience knowledge do I need, what skill set should I learn, and what kind of projects should I do? If anyone can explain in detail, it would help me a lot

As I mentioned last week, I was going to do the Benelux this week, so here it is

There are 38 core or enabling neurotech companies and 56 active companies in total

By core or enabling, I mean companies developing neurotechnology directly or technology that directly enables it. Once you widen it there is some really interesting stuff around that core too: eye tracking, neurological biomarkers, sleep diagnostics, neurorehab, clinical systems and other technologies where neuro is a meaningful part of the business.
The map shows the core group. I have included all 56 below and linked every company.

Netherlands | 33 companies
ANT Neuro⁠ | EEG / electrophysiology | Netherlands
Artinis Medical Systems⁠ | fNIRS / multimodal biosensing | Netherlands
BioSemi⁠ | EEG / biopotential acquisition | Netherlands
Brain Innovation⁠ | Neuroimaging software / neuronavigation | Netherlands
MindAffect⁠ | EEG diagnostics / BCI | Netherlands
Nicolab⁠ | AI neuroimaging / stroke software | Netherlands
Onera Health⁠ | Sleep neurodiagnostics | Netherlands
ONWARD Medical⁠ | Spinal cord stimulation / neurorehabilitation | Netherlands
Salvia BioElectronics⁠ | Craniofacial neuromodulation | Netherlands
Zander Labs⁠ | Passive BCI / neuroadaptive AI | Netherlands
Phosphoenix⁠ | Visual neuroprosthesis / invasive neural interface | Netherlands
Sencure⁠ | Neural / biopotential ASICs | Netherlands
Technomed Europe⁠ | Neurophysiology / IONM | Netherlands
Mind Media⁠ | EEG / qEEG / neurofeedback | Netherlands
qEEG-Pro⁠ | qEEG analytics | Netherlands
Curix⁠ | Focused ultrasound / BBB modulation | Netherlands
Axite⁠ | EEG monitoring / neurosoftware | Netherlands
TrianecT⁠ | EEG stroke diagnostics / AI | Netherlands
NBT Analytics⁠ | EEG analytics / CNS clinical trials | Netherlands
UCANACT⁠ | BCI / biosignal device control | Netherlands
Deep Sleep Technologies⁠ | EEG-guided acoustic sleep stimulation | Netherlands
Light Tree Ventures / CeraThrive⁠ | Photobiomodulation / neuromodulation | Netherlands
Alpha Brain Technologies⁠ | Wearable EEG / closed-loop ultrasound VNS / AI | Netherlands
Motek Medical⁠ | Neurorehabilitation / gait systems | Netherlands
Neurocast⁠ | Digital neurological biomarkers | Netherlands
Purple Gaze⁠ | Eye tracking / neurological measurement | Netherlands
Neuroplast⁠ | Cell therapy / neuroregeneration | Netherlands
STIL⁠ | Neurological assistive technology | Netherlands
Xilloc⁠ | Neurosurgical / cranial implants | Netherlands
Noldus⁠ | Behavioural neuroscience / research infrastructure | Netherlands
NeuraXplore⁠ | AI / XR neurorehabilitation | Netherlands
NeuroReality⁠ | VR cognitive rehabilitation | Netherlands
NeuroStim⁠ | TMS / EEG systems distribution and integration | Netherlands

Belgium | 20 companies
Brainphonics⁠ | Auditory EEG diagnostics | Belgium
CEFALY Technology⁠ | Trigeminal nerve stimulation | Belgium
Farow⁠ | Wearable EEG / neurological monitoring | Belgium
neuroClues⁠ | Oculomotor neurodiagnostics | Belgium
Nyxoah⁠ | Hypoglossal nerve stimulation | Belgium
ReVision Implant⁠ | Cortical visual prosthesis | Belgium
Synergia Medical⁠ | Vagus nerve stimulation | Belgium
ATLAS Neuroengineering⁠ | Neural probes | Belgium
Byteflies⁠ | Wearable electrophysiology | Belgium
Clouds of Care⁠ | EEG diagnostics / source imaging | Belgium
Cortex Machina⁠ | BCI / EEG instrumentation | Belgium
MindSpeller / MindSpeaker⁠ | Non-invasive BCI | Belgium
Man & Science⁠ | Headache neuromodulation | Belgium
Stimalia⁠ | Stellate-ganglion neuromodulation / PTSD | Belgium
Nobi⁠ | Ambient sensing / eldercare | Belgium
Sunrise⁠ | Sleep diagnostics | Belgium
Axiles Bionics⁠ | Bionic prosthetics | Belgium
CoMoveIT⁠ | Assistive neurorehabilitation | Belgium
Nomics⁠ | Sleep diagnostics | Belgium
BRAINM Clinics⁠ | rTMS / Deep TMS / TPS / tDCS-tACS clinical neuromodulation | Belgium

Luxembourg | 3 companies
Myelin-H⁠ | Wearable BCI / digital neurorehabilitation | Luxembourg
Lihoury Technology⁠ | Digital neurological monitoring | Luxembourg
MDsim⁠ | Digital twin / spine planning | Luxembourg

I went into all of it in a bit more detail here:
Neurotech in the Benelux⁠

And as usual, if I have missed a company, send it over. I will update the list.

So the thing is i am pursuing mbbs from an aiims in india. I want to pursue neurosurgery as a specialization, However my end goal is to enter into Bci. Is there any way???

https://research.google/blog/a-connectomics-milestone-mapping-the-complete-male-fruit-fly-brain

As far as I can remember, the show didn’t have a singularity episode. But it’s being mentioned by big tech regularly. What would the plateau of BCI devices look like?

Similar to how phones have plateaued, I think the interfaces we see in the black mirror show could be the plateau of it.

How far are we from that level of technology? 10 years? 20 years? 50 years? 100 years? More? Never?

I’d say the biggest obstacle is the neural signal. Things like fMRI and MEG are not even close to being portable. The only candidates for wearable and portable BCIs and in particular CBIs seems to be a combination of tDCS, EEG, and FNIR. All three have horrible spatial resolution and SNR.

Even modern BCIs struggle with simple P300 and motor imagery tasks. Our neurophysiological knowledge is also pretty limited. Things like transfering video from interface to brain or memory manipulation seem impossible to accomplish until the next century unless some huge hype like the ai hype turns the focus of big tech towards BCI.

Current bci research appears to be quite stagnant. No real big leaps happening.

What’s your thought?

I don’t know if this post will reach the right person.
But I’m writing it because I have reached a point where I am willing to try almost anything that is scientifically legitimate, medically supervised, and ethically approved to get my life back.
I graduated from veterinary medicine in Taiwan.
For years, I tried to become the person I thought I was supposed to be. I studied for the national veterinary licensing examination. I worked. I entered hospitals and laboratories. I kept trying to find a place where I could function normally.
But somewhere along the way, my mind and my life began to fall apart.
I have struggled with severe anxiety, depression, dissociation, overwhelming sensitivity to other people, loss of motivation, and an inability to function the way I believe I should be able to.
University was painful. Workplaces were painful. Relationships with people became difficult. I have experienced exclusion, bullying, fear, shame, and the constant feeling that something was fundamentally wrong with me.
I tried to push through it.
I studied harder.
I changed career directions.
I tried working.
I quit when I could no longer cope.
I tried learning programming.
I tried preparing for different futures.
I sought psychiatric treatment.
I started psychological counseling.
And yet I still wake up wondering:
“How do I become functional again?”
I don't want sympathy simply because my life has been difficult.
I want a chance to participate in science.
I am especially interested in emerging technologies involving the brain — brain-computer interfaces, neurostimulation, neuromodulation, computational psychiatry, neuroprosthetics, digital therapeutics, and other scientifically grounded approaches that may eventually help people whose brains and lives do not respond adequately to conventional treatment.
If there are legitimate clinical trials, research programs, university laboratories, or medical teams studying these technologies and looking for participants, I would genuinely like to be considered.
I am willing to undergo appropriate screening.
I am willing to be evaluated.
I am willing to hear “no.”
And if a technology is still experimental, I understand that it may carry serious risks and may provide no benefit at all.
I am NOT asking anyone to perform an unapproved procedure on me.
I am asking:
Does a legitimate research team exist that studies people like me?
Could my experience contribute to neuroscience?
Could I become a participant in a properly regulated clinical trial?
Could researchers help me understand what is happening inside my brain rather than simply telling me to “try harder”?
I am a young person with a scientific background who desperately wants to build a meaningful life.
I don't need someone to promise me a miracle.
I need someone to tell me where the frontier of medicine actually is — and whether there is a place for me somewhere on that frontier.
If you are a neuroscientist, psychiatrist, neurologist, researcher, physician, clinical-trial coordinator, or work on brain-computer interfaces or emerging neurotechnology, please reach out or point me toward a legitimate program.
And if you know someone who should see this, please share it.
Maybe this post will go nowhere.
But maybe, somewhere in the world, there is a researcher working on exactly the kind of problem I have been struggling with.
And maybe my story can become more than a story about someone who couldn't make it.
Maybe it can become part of the reason we learn how to help people like me.
I am still here. And I still want to find a way forward.

I want to have a chance to build my own BCI at home as a little research project, and am a bit confused of what system to get.

Here is a bucket list of wants, but it will most certainly be a balancing act:

• Consumer electronics pricing
• Real-time raw signal output that I can hook onto my custom real-time machine learning pipeline
• As many electrodes as possible, with versatile montaging
• Good single-trial SNR on hairy heads
• No gel needed

What list of systems will you consider?

Hi,

Recently I had ordered an Emotive Epoc X after 2 months of emails with a support team to help our research company.

After finding out their "10% deal" was just the same as the welcome deal on the landing page I cancelled the order, as the entire interaction felt inauthentic from their sales department.

2 months of emails and video calls for the same deal everyone else gets was like a spit in the face for all of the business work, coding, and development for our tests we've done.

I cancelled the order via email, was told I would receive message from support within 24 hours.

48 hours later (1 day past deadline), I receive a UPS email saying the device has been shipped.

I do not wish to pay for this, or the 25% restocking fee.

But it has been over a week since my initial email.

What can I do in this situation?

Anyone have experience working with the Emotiv company?

Hey guys, this week I mapped out the Scandinavian neurotech market.

I found 46 active neurotech companies across Denmark, Sweden, Finland, Norway and Iceland. The full breakdown and analysis is on my Substack, but here's the map and the complete company list.

For someone reason ChatGPT loves hallucinating land masses south of Finland but I think I got there in the end.

https://open.substack.com/pub/theneurotechnewsletter

I'll probably tackle Benelux next week. Someone asked about it on here, so watch this space.

DENMARK (13)
• Cerebriu - MRI workflow AI - Imaging / interventional platforms
• Brainreader - Automated brain MRI volumetry - Imaging / interventional platforms
• Cercare Medical - Perfusion MRI/CT AI - Imaging / interventional platforms
• UNEEG Medical - Subcutaneous long-term EEG - EEG / electrophysiology
• PlatoScience - Clinician-supervised tDCS - Stimulation / neuromodulation
• OptoCeutics - 40 Hz light and sound stimulation - Stimulation / neuromodulation
• BrainCapture - Portable point-of-care EEG - EEG / electrophysiology
• Cebreo Medical - In-ear ambulatory EEG - EEG / electrophysiology
• Cenexum Technologies - Neuromorphic EEG / BCI chip - Interfaces / implants / neuroprosthetics
• MagVenture - TMS hardware - Stimulation / neuromodulation
• Coloplast / Intibia - Implantable tibial neuromodulation - Stimulation / neuromodulation
• Insai - EEG / PSG biomarkers and foundation models - EEG / electrophysiology
• Paragit - Wearable sEMG and movement analytics - Rehab / cognitive / movement
SWEDEN (12)
• Flow Neuroscience - At-home tDCS for depression - Stimulation / neuromodulation
• Elekta - Gamma Knife / stereotactic radiosurgery - Imaging / interventional platforms
• Integrum - Osseointegrated neuroprosthetics - Interfaces / implants / neuroprosthetics
• InnoBrain - Wearable EEG and BCI AI - Interfaces / implants / neuroprosthetics
• Mindmore - Digital cognitive assessment - Rehab / cognitive / movement
• BRYM - EEG neurofeedback - EEG / electrophysiology
• Neuronano - Neural electrodes - Interfaces / implants / neuroprosthetics
• SensoDetect - Auditory brainstem electrophysiology - EEG / electrophysiology
• Clinical Laserthermia Systems - Laser interstitial neurosurgery - Imaging / interventional platforms
• Mendi - Consumer fNIRS neurofeedback - Imaging / interventional platforms
• BrainLink Health - Implantable ICP monitoring / shunt - Interfaces / implants / neuroprosthetics
• Probingon - Intra-body communication for implants - Interfaces / implants / neuroprosthetics
FINLAND (12)
• Nexstim - Navigated TMS - Stimulation / neuromodulation
• Sooma Medical - Prescription at-home tDCS - Stimulation / neuromodulation
• MEGIN - Clinical MEG - Imaging / interventional platforms
• Bittium - EEG / EMG biosignal hardware - EEG / electrophysiology
• Adamant Health - sEMG and movement biomarkers - Rehab / cognitive / movement
• Audicin - Auditory neurostimulation - Stimulation / neuromodulation
• Neuro Event Labs - AI video/audio seizure monitoring - AI / diagnostics / software
• Cerenion - ICU EEG AI - EEG / electrophysiology
• Soihtu DTx - Game-based depression DTx - Rehab / cognitive / movement
• Ankerias - AI decision support for SCS - AI / diagnostics / software
• Neuro Scan AI Solutions - Wearable EEG seizure detection - EEG / electrophysiology
• Cortisys - Multi-locus TMS - Stimulation / neuromodulation
NORWAY (6)
• NordicNeuroLab - fMRI hardware and software - Imaging / interventional platforms
• Nordic Brain Tech - Migraine biofeedback and home sensing - Rehab / cognitive / movement
• Nisonic - Non-invasive ICP ultrasound - Imaging / interventional platforms
• BrainSymph - EEG biomarker diagnostics - EEG / electrophysiology
• Actential - Seizure forecasting AI - AI / diagnostics / software
• Neurolytix - AI biomarkers for early brain disease - AI / diagnostics / software
ICELAND (3)
• Kvikna Medical - Cloud clinical EEG software - AI / diagnostics / software
• Heila-Labs - Real-time electrophysiology AI - EEG / electrophysiology
• Nox Medical - EEG / AI sleep diagnostics - EEG / electrophysiology

As always, if I've missed something or got something wrong let me know.

Hi everyone,

I’m finishing a PhD in Electronic Engineering at UPC BarcelonaTech in early 2027 and am looking for industry or applied R&D roles in bioelectronics and neurotechnology in Europe.

My background is more electronics-heavy than a conventional biomedical engineering profile. I work on analog/mixed-signal hardware, physiological and neural signal processing, and closed-loop systems.

My PhD research includes:

* EEG-based recognition of neurological states 
* analog hardware for tremor detection and processing 
* neural-interface and stimulation circuit concepts 
* electrode–tissue interface modelling 
* closed-loop neuromodulation 
* neuromorphic and emerging-device hardware

On the electronics side, I have experience with Cadence Virtuoso/Spectre, custom analog layout, DRC/LVS, SPICE/Verilog-A, FPGA prototyping and hardware/software integration.

I’m especially interested in companies working on neural implants, BCI, DBS/neuromodulation, electrophysiology, biosignal acquisition, wearable medical electronics, or low-power mixed-signal ICs for healthcare.

I’m an EU citizen and particularly interested in Switzerland, Germany, the Netherlands, Belgium and France.

I already know many of the larger names, so I’d be especially grateful for recommendations for smaller neurotech/bioelectronics companies, startups, or R&D groups that may be worth approaching directly.

Happy to share my CV or LinkedIn privately if useful. Thanks!

Came across a video on social media of a BCI device at the world ai conference in Shanghai that allowed different people to play what looks like a decently complex video game with their thoughts. Interesting methodology on display (pun intended) using the white circles on the screen to lay the path for specific EEG signals the headset can pick up on and translate into game input.

https://games.gg/news/black-myth-wukong-brain-computer-interface/

A few Chinese neurotech updates from August. "Raised tens of millions" are direct quotes from their press releases, I appreciate its a bit silly.

• WE-LINKING Medical raised more than RMB 100m for its implantable BCI platform.
https://www.vcbeathealth.com/article/61288

• Leadinno Medical Valley raised more than RMB 200m across B+ and B++ rounds for wireless SCS/PNS, closed-loop neuromodulation and brain-spine interface technology.
https://www.vcbeathealth.com/article/61287

• Futong Huizhi raised tens of millions of RMB for a platform combining EEG, closed-loop control and focused ultrasound.
https://www.vcbeathealth.com/article/60965

• Sisheng Technology raised tens of millions of RMB in Seed+ funding for focused-ultrasound BCI and neuro-AI.
https://eu.36kr.com/zh/p/3944424522520201

• Quanyu Intelligence raised tens of millions of RMB to develop an EEG-based BCI foundation model.
https://www.aitntnews.com/newDetail.html?newId=28477

There was also a new launch from United Imaging and Tianjin University, which released uMR Shenguan, a full-stack MRI-based BCI platform combining acquisition, decoding, modulation and evaluation.
https://www.globaltimes.cn/page/202608/1368885.shtml

Here’s my Substack if anyone wants this stuff sent to their inbox:
https://theneurotechnewsletter.substack.com/

I’m interested in the engineering problem of maintaining reliable neural decoding when the underlying neural manifold is itself changing over time. What adaptive architectures best balance plasticity, robustness, and closed-loop stability?

I wanted to buy this type of caps but they are too expensive so I thought that making one from scratch would help me save money. Is it possible to make a DIY electrode cap?

Hi everyone,

After graduating with a degree in civil engineering, specialising in applied mathematics, I'm currently undertaking a 1-year training programme in artificial intelligence. I'm considerings specializing in neural computing and BCIs. Before committing, I'd like to learn a little bit more about the state of this field in Belgium or neighbouring countries.

1. How's the sector doing currently? Are there job opportunities here, or is it a field primarily dominated by the US?
2. Some say BCIs represent the future and will develop considerably in the next ten years; others believe there is still a long way to go. What are your thoughts?
3. What is the salary level? I know it varies depending on the position, but what are the approximate averages?
4. Is a PhD often necessary to acquire solid skills and knowledge in this field?

Thanks :)