Project E: Intracortical data
Human motor cortex recordings from published BCI studies, downloaded free. Spike sorting, local field potentials, and a decoder on the real thing. The end of the road that started with a cockroach leg.
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The DANDI archiveDANDI archiveA public repository of neurophysiology data in NWB format, including human intracortical BCI recordings from published papers. Glossary entry and the data supplements of recent Nature papers hold recordings from Utah ArraysUtah ArrayThe 10-by-10 grid of 1.5 mm silicon needles invented at the University of Utah, the electrode in nearly every human intracortical BCI study to date. Glossary entry in the motor cortex of people with paralysis: the actual data behind the cursor and speech-decoding results. In NWBNeurodata Without Borders (NWB)The standard file format for neurophysiology data, so recordings from different labs can be read the same way. Glossary entry format, with the behaviour aligned. You have built every tool needed to work with them: spike detection from Phase 1’s thinking, the Kalman decoder from Phase 3, the population analysis from Phase 4. Now run them on a human brain.
A Utah Array has 96 recording electrodes. In a participant implanted three years ago, roughly how many electrodes still show clearly sortable single neurons?
Often a dozen or two clean single units, with many more channels carrying threshold crossings from several unsortable neurons and some carrying only field potentials. Recordings degrade over years as the tissue responds and electrodes fail, which is why most human BCI decoders now use threshold crossings and spike-band power rather than sorted units, and why the foreign body response is the field’s central engineering problem.
The data
Search DANDI for human intracortical datasets: the BrainGate consortium has released cursor-control and handwriting data; the Willett et al. 2023 speech dataset and the Metzger et al. 2023 dataset are public through Dryad and the papers’ supplements. The Neural Latents Benchmark packages several (mostly monkey) datasets in a standard format with held-out evaluation, which is the right place to compare a decoder against the field. Load with pynwb or the NLB tools.
Three analyses
Signal quality across channels. For each electrode: the spike-band (300 to 3000 Hz) RMS, the threshold-crossing rate, and, where waveforms are provided, a sorting. Plot the array as a 10-by-10 grid coloured by signal quality. Count the clean single units. Compare to the paper’s methods section. This grid is the honest picture of what a chronic array delivers.
Decode kinematics. On a cursor or handwriting dataset: bin threshold crossings at 20 ms, fit the Kalman filter from Phase 3 on the calibration block, test on the held-out block, report correlation between decoded and true velocity. Then fit the same on sorted units only and compare; then on spike-band power; then on LFP features. The comparison says which signal carries the information, and it usually surprises people how well the unsorted signals do.
Population dynamics. PCA on the binned population activity during reaches. Plot the trajectories per reach direction. See the rotational structure that the neural dynamics literature describes, in a human motor cortex. Then try a latent-variable model (the NLB baselines include several) and report held-out likelihood.
Speech
If you take on the speech dataset: neural features per 20 ms, attempted-speech phoneme labels, a recurrent network mapping one to the other, a language model on top. Weeks, not days, and the result is a reimplementation of a paper that made headlines. Report phoneme error rate and word error rate alongside the paper’s.
What you will understand at the end
What a human intracortical recording actually looks like after years, and why decoders were redesigned to cope. Why threshold crossings won. How much of the population’s information lives in a few dimensions. And that the arc from a cockroach leg on a speaker to a paralyzed person’s motor cortex on your laptop is one arc, made of the same physics, the same amplifiers, and the same statistics.
- Choose a dataset and load it. Plot raw traces from a good channel and a bad one.
- Build the array quality grid. Count units.
- Kalman decoder on threshold crossings, held-out block; then on sorted units, spike-band power, and LFP. Table.
- PCA trajectories by reach direction.
- Optionally, the speech decoder.
- Write it up as a reproduction, in the Phase 3 format.