Exploring the Limits: A Comprehensive Look into Brain-Computer Interfaces (BCIs)

Brain-Computer Interfaces (BCIs) represent a direct communication pathway between the human brain and external devices. These extraordinary systems function by interpreting brain signals, allowing users to carry out tasks without physical movements. From neurorehabilitation to enhancing human cognition, the potential applications are limitless.??

??Diving Deeper: EEG Paradigms??

Electroencephalogram (EEG) paradigms are crucial for BCIs as they are responsible for detecting and interpreting brain activity. Different types of EEG signals, such as Alpha waves, Motor Imagery, and SSVEP/ASSR, each come with their unique characteristics and applications:

• Alpha wave paradigm: Known for its strength and simplicity, it's typically associated with relaxation and can be measured almost all over the scalp. However, it may not offer a detailed insight into specific cognitive processes.??
• Motor Imagery: This exciting paradigm can potentially control wheelchairs or mice using thoughts alone. However, due to high inter-individual variability, achieving accurate results is challenging.??
• SSVEP/ASSR: These paradigms respond to visual and auditory stimuli respectively. They boast a high Signal-to-Noise Ratio (SNR), although the latter may lead to tinnitus in subjects.??

??Beyond the Brain: Other Modalities ???

BCIs aren't restricted to just brain activity; other physiological signals are equally valuable. From facial expressions (fEMG) to heart dynamics (PPG), these modalities offer a well-rounded approach to data collection:

• Facial expressions classification (fEMG): This technique can accurately identify emotions but requires subjects to have visible facial expressions, which might not always be practical. ????
• Heart dynamics (PPG): A non-invasive method to measure cardiovascular activity and stress levels. However, it may be influenced by external factors like temperature and physical activity.??
• Electrooculography (EOG): Useful for tracking eye movements, but its accuracy may decrease with user fatigue. ???

?? P300 [12] ??: The P300 speller is a classic application [13] ???, but the backbone is the oddball paradigm [14] ??. The best consumer-oriented implementation of the paradigm to BCI probably belongs to Neurable [15] ??. While I haven’t compared them myself, my knowledge of SSVEP leads me to conclude that SSVEP probably offers a higher brain-computer information transfer rate ??. Still, this is a core BCI paradigm.

P300 speller in action ??

Technical considerations ???:

• P300 is generated in the parietal lobe. You need good scalp coverage ?? ??.
• Time-alignment of responses is key ?? and software/transmission delay and jitter need to be addressed and accurate in milliseconds.
• You will benefit from using a dimensionality reduction approach such as Factor Analysis, before attempting classification ??.
• A linear classifier should be able to perform accurately on reduced feature space ??.

?? Cognitive state monitoring(stress/relaxation/engagement/motivation/cognitive load) ?? ??: These cognitive states are passive paradigms and can be decoded efficiently using EEG. These signals are the most represented among today’s consumer-grade EEG systems. (Neurosity, Muse, Neurable) [16,17,18]. There is an extensive literature on the topic ??.

Technical considerations ???:

• Except for the alpha wave, which is often considered a correlate of relaxation, most of these are better sampled frontally ??.
• Gamma band amplitude is a good predictor for these metrics, but often ends up being buried in noise, when using dry electrode systems ??.
• Most of these metrics are based on relative powerband amplitude ??.

?? Facial expressions classification ??: Facial expressions analysis is commonly done using cameras, the best system on the market is likely to be [27], but there are more and more available options. There are various opinions regarding their meaning. Personally, I find them to be very informative about the emotional reactions of subjects and convey pertinent information about their state of mind ??????.

?? Arousal (from EDA) ??: Arousal can be estimated using EEG, but EDA reacts firmly to arousing stimuli. These events can be used to establish precisely which stimuli are the most likely to be responsible for distracting the subject ??.

?? Heart dynamics convey a lot of information about metabolism and stress levels ????: It has been demonstrated to react to valence, but the latter is hard to use because it gets buried under all the other effects that change heart rate.

??? Electrooculography (EOG) ??: I have played with EOG much, but I plan to. In the meantime, eye movements are a well-known artifact, when sampling EEG. It measures eye movement, but I don’t know how accurate it can be as an eye-tracker.

There are at least two other paradigms that I’m aware of, although I never studied them in detail ??? ♀?:

• ?? Near Infrared Spectroscopy (NIRS): This is an interesting one, as it measures oxygenation changes in the cortex, which correlate with cognitive load.
• ?? Magnetic Encephalography (MEG): This one measures the magnetic field created by the electrical activity in the brain. It’s far more expensive than EEG, but has better spatial resolution.

The future of BCI is exciting and holds great potential for various applications ??. Technology continues to evolve and adapt, making it more accessible and useful for everyone ??.

?? Alpha wave paradigm: If you are getting started in BCI, this is the first paradigm you should get familiar with. The alpha wave is best measured toward the back of the head (visual system, occipital lobe) but is so strong that can be measured almost all over the scalp. The paradigm is simple, when you close your eyes, the power in the alpha band goes up. When you open your eyes, it goes down [1]??. You can’t miss it. Starting with this paradigm will help you develop your first pipeline and get your tools straight. And because it’s an easy one, you will be rewarded quickly ??.

Technical considerations ???:

• Works better if you sample the occipital lobe but can be measured all the way to the parietal lobe ??.
• Make sure to reject eye-blinks as they overlap in the frequency domain ??.

Pipeline Overview ??:

• Extract alpha powerband, with a moving window (1- to 5-seconds width) ?.
• This feature can be classified accurately using any linear model ??.
• You can use normal distribution Bayesian classifier (a non-central f-distribution is more precise but expect marginal performance improvement) ??.

?? Motor Imagery [2]: This is the dream of many BCI developers. Imagine controlling a wheelchair or a mouse, using only your thoughts ??. Motor imagery has been tried and tried again. Truth is, it works, but not as well as we would like it to (in-brain implants perform strongly here) ??.

You can expect to classify between right hand, left hand or feet, under good conditions, but unless you equip yourself with a high-density and wet electrode research grade system, that’s as far as you’ll go ??.

This is one of the hard problems of BCIs. If you want to give it a try, I suggest you start by working on existing datasets [3], to save you the logistics of recording your own. When you’ll achieve satisfying results, then you can move on to develop an online acquisition and modelling process ??.

Technical considerations ???:

• You need to sample the motor cortex ??.
• You need a high-end consumer-grade system or a research-grade system ??.
• Inter-individual variability is a big issue ?? ♂??? ♀?.
• Don’t expect to score much above 66%, real-time single trial classification (3 classes) ??.

Pipeline Overview ??:

• I suggest you take a look at pyRiemman [4, 5, 6] ??.
• I’m ashamed of code quality, but I got a good score with deep learning [7] ??.
• The pipeline depends extensively on the classification strategy you chose ??.

?? SSVEP, ASSR [8, 10]: These two paradigms are similar. SSVEP applies to visual stimuli, while ASSR applies to auditory responses. In my opinion, the best implementation of the SSVEP as a user interface belongs to NextMind [9]. SSVEP has a very high SNR, making it easy to detect. ASSR works well but the subject is likely to experience tinnitus and the stimuli are very annoying to hear. I might be missing something, but it didn’t appear to have much potential, beyond medical applications (see cocktail party problem for a high-potential auditory BCI application) ??.

I read that SSVEP amplitude is modulated by attention [11]. On average, it is probably true, but in my experience, inter-individual variation and even inter-trial variations largely dominate amplitude variations, making it hard to extract attentional signal ??.

Technical considerations ???:

• SSVEP requires electrodes above the occipital lobe but can be measured all the way up to temporal and parietal lobe (lower SNR) ??.
• ASSR is better measured above temporal lobe ??.

Pipeline Overview ??:

• Basic SSVEP, ASSR elicit a very narrow-band power in the frequency domain ??.
• I got very good results using f-ratio statistics [10]. By comparing the stimulated frequency with the surrounding frequency powers. Using this approach, you don’t even need machine learning ??.

Ethical Considerations in BCI Research

As with any transformative technology, BCIs carry significant ethical implications. These include concerns over privacy, as BCIs can potentially access our innermost thoughts and intentions, and issues of equity, considering the possible divide that could emerge between those who can afford these technologies and those who can't. Additionally, there are challenges related to personal identity and cognitive liberty: how might our sense of self-change if our brain could be directly interfaced with machines, and who has the right to access and control our neural data?

Future Applications of BCIs????

The potential applications of BCIs are vast and varied. They range from medical uses, such as helping those with mobility issues regain independence, to entertainment applications, such as gaming and virtual reality experiences. BCIs could also transform the way we work, by enhancing cognitive abilities and enabling more direct and efficient interactions with technology. As the technology matures, we might also see the rise of entirely new applications that are difficult to predict at this stage.

Further Resources

For those who are interested in diving deeper into the world of BCIs, there are a wealth of resources available. Books such as "The Future of the Brain: Essays by the World's Leading Neuroscientists" offer a comprehensive overview of the field, while organizations like the International Brain-Computer Interface Society provide regular updates on the latest research. For hands-on learning, there are various online courses and workshops on platforms like Coursera and edX that cover both the fundamentals and advanced topics in BCI.??

Sources:

[1] Adrian, E.D., & Matthews, B.H. (1934). The Berger rhythm: potential changes from the occipital lobes in man. Brain, 57(4), 355-385.

[2] Pfurtscheller, G., & Neuper, C. (2001). Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 89(7), 1123-1134.

[3] Motor Imagery Datasets: PhysioNet (Link to the datasets)

[4, 5, 6] Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2010). Riemannian geometry applied to BCI classification. International Conference on Latent Variable Analysis and Signal Separation.

[7] Deep Learning Application in BCI (Link to the code)

[8, 10] Middendorf, M., McMillan, G., Calhoun, G., & Jones, K.S. (2000). Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Transactions on Rehabilitation Engineering, 8(2), 211-214.

[9] NextMind’s Direct Brain Command platform (Link to the product)

[11] Müller-Putz, G.R., Scherer, R., Brauneis, C., & Pfurtscheller, G. (2005). Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. Journal of Neural Engineering, 2(4).

[12] Farwell, L.A., & Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70(6), 510-523.

[13] Pfurtscheller, G., Allison, B.Z., Brunner, C., Bauernfeind, G., Solis-Escalante, T., Scherer, R., Zander, T.O., Mueller-Putz, G., Neuper, C., & Birbaumer, N. (2010). The hybrid BCI. Frontiers in Neuroscience, 4, 30.

[14] Sutton, S., Braren, M., Zubin, J., & John, E.R. (1965). Evoked-potential correlates of stimulus uncertainty. Science, 150(3700), 1187-1188.

[15] Neurable’s EEG BCI platform (Link )

[16] Neurosity’s Notion(Link )

[17] Muse Headband (Link)

[18] Neurable’s BCI for VR/AR (Link)

[27] Facial Expression Analysis System (Link)

#brain-computer-interface #BCI #EEG #motorimagery #SSVEP #ASSR #P300 #NIRS #MEG #ethicalconsiderations #privacy #equity #personalidentity #cognitiveliberty #futureapplications