Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria

Abstract

Background: Technology to restore the ability to communicate in paralyzed persons who cannot speak has the potential to improve autonomy and quality of life. An approach that decodes words and sentences directly from the cerebral cortical activity of such patients may represent an advancement over existing methods for assisted communication.

Methods: We implanted a subdural, high-density, multielectrode array over the area of the sensorimotor cortex that controls speech in a person with anarthria (the loss of the ability to articulate speech) and spastic quadriparesis caused by a brain-stem stroke. Over the course of 48 sessions, we recorded 22 hours of cortical activity while the participant attempted to say individual words from a vocabulary set of 50 words. We used deep-learning algorithms to create computational models for the detection and classification of words from patterns in the recorded cortical activity. We applied these computational models, as well as a natural-language model that yielded next-word probabilities given the preceding words in a sequence, to decode full sentences as the participant attempted to say them.

Results: We decoded sentences from the participant's cortical activity in real time at a median rate of 15.2 words per minute, with a median word error rate of 25.6%. In post hoc analyses, we detected 98% of the attempts by the participant to produce individual words, and we classified words with 47.1% accuracy using cortical signals that were stable throughout the 81-week study period.

Conclusions: In a person with anarthria and spastic quadriparesis caused by a brain-stem stroke, words and sentences were decoded directly from cortical activity during attempted speech with the use of deep-learning models and a natural-language model. (Funded by Facebook and others; ClinicalTrials.gov number, NCT03698149.).

Copyright © 2021 Massachusetts Medical Society.

Figures

Figure 1.. Schematic Overview of the Direct Speech Brain–Computer Interface.

Shown is how neural activity acquired from an investigational electrocorticography electrode array implanted in a clinical study participant with severe paralysis is used to directly decode words and sentences in real time. In a conversational demonstration, the participant is visually prompted with a statement or question (A) and is instructed to attempt to respond using words from a predefined vocabulary set of 50 words. Simultaneously, cortical signals are acquired from the surface of the brain through the electrode array (B) and processed in real time (C). The processed neural signals are analyzed sample by sample with the use of a speech-detection model to detect the participant’s attempts to speak (D). A classifier computes word probabilities (across the 50 possible words) from each detected window of relevant neural activity (E). A Viterbi decoding algorithm uses these probabilities in conjunction with word-sequence probabilities from a separately trained natural-language model to decode the most likely sentence given the neural activity data (F). The predicted sentence, which is updated each time a word is decoded, is displayed as feedback to the participant (G). Before real-time decoding, the models were trained with data collected as the participant attempted to say individual words from the 50-word set as part of a separate task (not depicted). This conversational demonstration is a variant of the standard sentence task used in this work, in that it allows the participant to compose his own unique responses to the prompts.

Figure 2.. Decoding a Variety of Sentences in Real Time through Neural Signal Processing and Language Modeling.

Panel A shows the word error rates, the numbers of words decoded per minute, and the decoded sentence lengths. The top plot shows the median word error rate (defined as the number of word errors made by the decoder divided by the number of words in the target sentence, with a lower rate indicating better performance) derived from the word sequences decoded from the participant’s cortical activity during the performance of the sentence task. Data points represent sentence blocks (each block comprises 10 trials); the median rate, as indicated by the horizontal line within a box, is shown across 15 sentence blocks. The upper and lower sides of the box represent the interquartile range, and the I bars 1.5 times the interquartile range. Chance performance was measured by computing the word error rate on sentences randomly generated from the natural-language model. The middle plot shows the median number of words decoded per minute, as derived across all 150 trials (each data point represents a trial). The rates are shown for the analysis that included all words that were correctly or incorrectly decoded with the natural-language model and for the analysis that included only correctly decoded words. Each violin distribution was created with the use of kernel density estimation based on Scott’s rule for computing the estimator band-width; the thick horizontal lines represent the median number of words decoded per minute, and the thinner horizontal lines the range (with the exclusion of outliers that were more than 4 standard deviations below or above the mean, which was the case for one trial). In the bottom chart, the decoded sentence lengths show whether the number of detected words was equal to the number of words in the target sentence in each of the 150 trials. Panel B shows the number of word errors in the sentences decoded with or without the natural-language model across all trials and all 50 sentence targets. Each small vertical dash represents the number of word errors in a single trial (there are 3 trials per target sentence; marks for identical error counts are staggered horizontally for visualization purposes). Each dot represents the mean number of errors for that target sentence across the 3 trials. The histogram at the bottom shows the error counts across all 150 trials. Panel C shows seven target sentence examples along with the corresponding sentences decoded with and without the natural-language model. Correctly decoded words are shown in black and incorrect words in red.

Figure 3.. Distinct Neural Activity Patterns during Word-Production Attempts.

Panel A shows the participant’s brain reconstruction overlaid with the locations of the implanted electrodes and their contributions to the speech-detection and word-classification models. Plotted electrode size (area) and opacity are scaled by relative contribution (important electrodes appear larger and more opaque than other electrodes). Each set of contributions is normalized to sum to 1. For anatomical reference, the precentral gyrus is highlighted in light green. Panel B shows word confusion values computed with the use of the isolated-word data. For each target word (each row), the confusion value measures how often the word classifier predicted (regardless of whether the prediction was correct) each of the 50 possible words (each column) while the participant was attempting to say that target word. The confusion value is computed as a percentage relative to the total number of isolated-word trials for each target word, with the values in each row summing to 100%. Values along the diagonal correspond to correct classifications, and off-diagonal values correspond to incorrect classifications. The natural-language model was not used in this analysis.

Figure 4.. Signal Stability and Long-Term Accumulation of Training Data to Improve Decoder Performance.

Each bar depicts the mean classification accuracy (the percentage of trials in which the target word was correctly predicted) from isolated-word data sampled from the final weeks of the study period (weeks 79 through 81) after speech-detection and word-classification models were trained on different samples of the isolated-word data from various week ranges. Each result was computed with the use of a 10-fold cross-validation evaluation approach. In this approach, the available data were partitioned into 10 equally sized, nonoverlapping subsets. In the first cross-validation “fold,” one of these data subsets is used as the testing set, and the remaining 9 are used for model training. This was repeated 9 more times until each subset was used for testing (after training on the other subsets). This approach ensures that models were never evaluated on the data used during training (Sections S6 and S14). I bars indicate the 95% confidence interval of the mean, each computed across the 10 cross-validation folds. The data quantities specify the average amount of data used to train the word-classification models across cross-validation folds. Week 0 denotes the first week during which data for this study was collected, which occurred 9 weeks after surgical implantation of the study device. Accuracy of chance performance was calculated as 1 divided by the number of possible words and is indicated by a horizontal dashed line.