I spent a solid 6 months intimately learning the pros and cons of various existing BCI systems, as well as the exact methods researchers use to make their technology look like a breakthrough when it isn't.
Specifically, here, they've created a system that can decode one of 50 symbols - a mere 1 bit more than the English alphabet - and described the symbols as "words" so that the reader thinks each symbol carries much more information than it actually does. They've also cherry-picked the patient that responded the best.
When you peel back the hype, this is about the same performance we've been getting since '08 for an invasive, subdural, non-penetrative array.
Anyway, knock yourself out with research: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&as_yhi...
Here's one from 2006 that got 35bpm without an implant (the one in the article would be closer to 60, but that's to be expected as it's invasive): https://d1wqtxts1xzle7.cloudfront.net/46069183/the_berlin_br...
Here's another from 2010 with a similar result under similar conditions: https://pure.ulster.ac.uk/ws/files/11410334/cecotti_tnsre.pd...
Within the abstract:
> The average accuracy and information transfer rate are 92.25% and 37.62 bits per minute, which is translated in the speller with an average speed of 5.51 letters per minute.
5.51 letters per minute, not words. The work you cited is not comparable to the UCSF work at all.
It seems you are measuring performance in terms of bit-rate (i.e. based on how many symbols per minute), which makes sense when you are using a cursor-based speller.
This approach is not a correct measurement of bitrate with this speech-motor decoder, however, as words are being decoded based on the syllables contained within it. The decoding model is trained to recognize 50 specific combinations of syllables, and the total number of unique single syllable phonemes is about 44.
Again, it is misleading at best to user a measure of "words per second" when you're restricted to a set of 50 of them. A keyboard that had both English and Cyrillic characters in it would have 59 unique symbols.
>It seems you are measuring performance in terms of bit-rate (i.e. based on how many symbols per minute), which makes sense when you are using a cursor-based speller.
I genuinely fail to see what the cursor has to do with anything. Communication speed is communication speed.
>This approach is not a correct measurement of bitrate with this speech-motor decoder, however, as words are being decoded based on the syllables contained within it.
Just as symbols in cursor tasks are decoded based on relative position?
i think it gets rather muddy as there isn't really a good metric for raw signal quality (afaik). there's cell tuning and number of spiking channels, but still not a great measure of snr for bmi work (afaik). often times people will apply measures to the outputs of their systems, like task performance, but part of the problem there is that often the state model has varying quality and suitability to task, so it can be difficult to disambiguate signal quality from state model performance.
(of course, in speech recognition they don't care, the game is to minimize WER and maximize decoding speed and whether language or acoustics (at least when they were separate) get you there, it doesn't matter)
This was something I picked up on back in 2017. I did manage to come up with a definition of SNR that made some sense (basically the euclidean distance between symbol menas, divided by the noise level along the vector connecting the two symbols, assuming the feature space was basically an N-dimensional QAM signal using features 1...N instead of amplitude and phase) - but even then that didn't take into account the fact that the noise was neither well-approximated by AWGN biased nor even constant...
And of course, as you said, you could get a bad SNR just because you're extracting the wrong features (although, to be fair, the same problem can exist in telecoms too).
> a nurse a laminated piece of A4 paper and a patient who can blink get about 15 characters
How is this a relevant comparison? 15 characters per minute is much less than the 15 WORDS per minute performance this work demonstrates
> I spent a solid 6 months intimately learning the pros and cons of various existing BCI systems, as well as the exact methods researchers use to make their technology look like a breakthrough when it isn't.
Then you should know this is a huge deal.
> Specifically, here, they've created a system that can decode one of 50 symbols
Previous motor and speech neuroprosthetic systems focused on pointing (controlling cursor) and more recently, handwriting (https://www.nature.com/articles/s41586-021-03506-2). This work goes gives communication rate similar to the handwriting work, but decoding speech from the motor cortex has been much less understood than that of simple motor movements such as cursor position and velocity control.
Even more impressive, the test subject is not even a native English speaker.
> They've also cherry-picked the patient that responded the best.
Bravo-1 is the only subject they had.
> When you peel back the hype, this is about the same performance we've been getting since '08 for an invasive, subdural, non-penetrative array.
This is completely, utterly false. See (https://stacks.stanford.edu/file/druid:jx921pv3255/Technical...) for survey of performance of typing BCI.
You seem to love to cite your background as a BCI PhD dropout. I should also point out that I have a completed PhD in invasive neuroprosthetics and still work in the field (not that matters when anyone can look up the sources and judge for themselves).
15 words from a set of 50 (6 bits per "word" vs. 5 per letter in the English alphabet). It's like saying a 100 baud telegraph machine can decode 300 words per second, just so long as those words come from the set of "dot" and "dash".
>but decoding speech from the motor cortex has been much less understood than that of simple motor movements such as cursor position and velocity control.
Cursor position and velocity are outputs, the input is still a self-paced motor imagery task.
>Bravo-1 is the only subject they had.
Fair cop. Maybe they'll get genuinely impressive results with their next patient.
>This is completely, utterly false. See (https://stacks.stanford.edu/file/druid:jx921pv3255/Technical...) for survey of performance of typing BCI.
I posted a link in a comment below showing an ITR of 35bpm from scalp EEG from pre-2010.
>I have a completed PhD in invasive neuroprosthetics and still work in the field
I am truly sorry for your loss.