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Offenberg, E. C.

Publications and source records attributed to Offenberg, E. C..

2 recordsLinked to original sources

Long-term independent use of an intracortical brain-computer interface for speech and cursor control

Brain-computer interfaces (BCIs) can provide naturalistic communication and digital access to people with severe paralysis by decoding neural activity associated with attempted speech and movement. Recent work has demonstrated highly accurate intracortical BCIs for speech and cursor control, but two critical capabilities needed for practical viability were unmet: independent at-home operation without researcher assistance, and reliable long-term performance supporting accurate speech and cursor decoding. Here, we demonstrate the independent and near-daily use of a multimodal BCI with novel brain-to-text speech and computer cursor decoders by a man with paralysis and severe dysarthria due to amyotrophic lateral sclerosis (ALS). Over nearly two years, the participant used the BCI for more than 3,800 cumulative hours to maintain rich interpersonal communication with his family and friends, independently control his personal computer, and sustain full-time employment - despite being paralyzed. He communicated 183,060 sentences - totaling 1,960,163 words - at an average rate of 56.1 words per minute. He labeled 92.3% of sentences as being decoded at least mostly correctly. In formal quantifications of performance where he was asked to say words presented on a screen, attempted speech was consistently decoded with over 99% word accuracy (125,000 word vocabulary). The participant also used the speech BCI as keyboard input and the cursor BCI as mouse input to control his personal computer, enabling him to send text messages, emails, and to browse the internet. These results demonstrate that intracortical BCIs have the potential to support independent use in the home, marking a critical step toward practical assistive technology for people with severe motor impairment.

neuroscience↗

Optimal positioning and size of high-density electrocorticography grids for speech brain-computer interfaces

Speech-based brain-computer interfaces (BCIs) can offer an intuitive means of communication for those who have lost the ability to speak due to paralysis. Significant progress has been made in classifying individual words from high numbers of electrocorticographic (ECoG) electrodes on the sensorimotor cortex (SMC). As implantations of larger grids with more ECoG electrodes are associated with higher surgical risk, we here examined whether confined electrode configurations can match the classification accuracy of larger grids. To this end, we analyzed data from eight able-bodied participants with high-density ECoG grids (64 to 128 electrodes) who performed a task that involved speaking 12 Dutch words. Word pronunciation was associated with changes in high frequency band activity in two SMC foci, one in the ventral SMC and another in the dorsal SMC. Using a combinatorics approach, we found that a smaller, rectangular, configuration with a surface area of 325 mm2 to 561 mm2 (32 electrodes) could achieve a word classification accuracy similar to that of the larger grids: 76{+/-}16% versus 75{+/-}17% across participants, respectively (practical chance level 16.7%). The best configurations were oriented vertically and centered on the central sulcus. These findings indicate that a 32-electrode ECoG grid placed optimally can be sufficient for achieving high word classification accuracy on a closed set of words. We conclude that targeted placement of small ECoG grids can reduce surgical demands on end users and justify energy- and complexity-efficient designs of fully implantable BCI devices for individuals with severe paralysis.

neuroscience↗