DOI: 10.5061/dryad.hqbzkh1p6
title: Plug and play stability for intracortical brain-computer interfaces: A one-year demonstration of seamless brain-to-text communication

description: Intracortical brain-computer interfaces (iBCIs) have shown promise for
 restoring rapid communication to people with neurological disorders such
 as amyotrophic lateral sclerosis (ALS). However, to maintain high
 performance over time, iBCIs typically need frequent recalibration to
 combat changes in the neural recordings that accrue over days. In this
 study, we propose a method: Continual Online Recalibration with
 Pseudo-labels (CORP), that enables self-recalibration of communication
 iBCIs without interrupting the user. We evaluated CORP with one clinical
 trial participant. CORP achieved a stable decoding accuracy of 93.84% in
 an online handwriting iBCI task, significantly outperforming other
 baseline methods. This dataset contains 21 sessions of recorded neural
 activities used for the evaluation. It has been formatted for developing
 and evaluating machine learning models. There 5 more sessions heldout for
 a planned iBCI stability competition. They will be released in the future.
 We also provide a pretrained RNN seed model and a laugnage model to
 preproduce the results in our paper.
description: # Data for: Plug-and-Play Stability for Intracortical Brain-Computer
 Interfaces: A One-Year Demonstration of Seamless Brain-to-Text
 Communication https://neurips.cc/virtual/2023/poster/71605 Brain-computer
 interfaces (BCIs) can restore communication to people who have lost the
 ability to move or speak. However, BCIs typically require frequent
 recalibration to maintain high-performance. We proposed a new method:
 Continual Online Recalibration with Pseudo-labels (CORP) that can
 automatically recalibrate handwriting BCIs without interrupting the user.
 We demonstrated CORP on a handwriting BCI for a person with tetraplegia
 for one year. The BCI was able to maintain high performance for 57 weeks
 without manual recalibration. This repository contains the data for
 reproducing the results in the paper. ## Description of the data and file
 structure ### Neural Data The neural data are organized into two folders:
 seed\_model\_training\_data, and online\_evaluation\_data. The
 seed\_model\_training\_data folder contains the data used to train the
 seed model. The online\_evaluation\_data folder contains the data used to
 evaluate CORP online. #### Seed Model Training Data We collected 11
 sessions of instructed delay sentence copying task data from participant
 T5. In each session, T5 was instructed to copy sentence on a compunter
 screen while we recorded his brain activity. The data were recording using
 two 96-channel Blackrock Utah Arrays. Data was collected in a series of
 'blocks' (10-20 trials in each block), in between which T5
 rested. On each trial, T5 first saw a red square with a sentence above it.
 Then, when the square turned green, T5 attempted to copy the sentence by
 handwriting it letter by letter. The 'mat' subfolder contains
 the data for the instructed delay sentence copying task. Each file
 'mat' file is one session, containing the following fields: *
 `tx_feats': list of 2D arrays. Each 2D array is the binned (20ms)
 threshold crossing features (-4.5 RMS) for one trial during go period. The
 2D array has shape (n\_timesteps, n\_channels). * `sentences': list
 of strings. Each string is the sentence that T5 was instructed to copy on
 one trial. * `blocks' : list of integers. Each integer is the block
 number that the trial belongs to. The `tfrecord' folder contains the
 same data as the mat folder, but converted to tfrecord format for training
 the seed model. #### Online Evaluation Data We colected 15 sessions of
 instructed delay sentence copying task data from T5. The task has the same
 structure as the seed model training data collection task, except that T5
 could see real-time decoded results while copying. In each session, we ran
 two kinds of blocks. In the no-recalibration block, the neural activity
 was decoded with a fronzen seed model. In the recalibration block, we used
 CORP to continually update the decoder model. The `no_recalibration and
 recalibration' subfolders contain the data for each kind of block. In
 the `no_recalibration' and 'recalibration' folders, the
 data is organzied the same as the seed_model_training_data folder. Each
 file is one session, containing the following fields: * `tx_feats':
 list of 2D arrays. Each 2D array is the binned (20ms) threshold crossing
 features (-4.5 RMS) for one trial during go period. The 2D array has shape
 (n\_timesteps, n\_channels). * `sentences' : list of strings. Each
 string is the sentence that T5 was instructed to copy on one trial. *
 `blocks' : list of integers. Each integer is the block number that
 the trial belongs to. * `rnn_decoded' : list of strings. Each string
 is the raw RNN decoded sentence for one trial. * `lm_decoded' : list
 of strings. Each string is the language model decoded sentence for one
 trial. **Note that we only release the first 10 sessions in the version.
 The remaining 5 sessions will be used for a competition and will be
 released later.** ### Additionally, we offer two files to help reproduce
 the results in the paper: #### Pretrained Seed Model This folder contains
 a pretrained seed model in Tensorflow. See
 [repo](https://github.com/cffan/CORP) about its usage. #### Launguage
 Model This folder contains the 3-gram language model used during online
 evaluation. See [repo](https://github.com/cffan/CORP) about its usage. ##
 Code/Software This [repo](https://github.com/cffan/CORP) contains code for
 reproducing the results in the paper.