Decoding attempted handwritten characters based on temporal endovascular neural features

We evaluated temporal neural features for motor decoding using a minimally-invasive endovascular brain-computer interfaces (BCI). We hypothesised that relatively long compound movements generate distinct patterns of neural activity that are encoded by temporal features. Approach. A single participant implanted with the Stentrode endovascular BCI attempted to hand write English characters, which were then classified offline and online based on the recorded brain activity. The input to the model was a one-dimensional trajectory of high-gamma power, summed across all channels to isolate the effect of temporal information on decoding performance. Main results. A small number of classes across multiple months was reliably decoded, showing that temporal decoding could identify stable patterns of brain activity associated with each handwriting movement. Across four online sessions, where each session contained 20-30 trials/character and 80-180 rest trials, the best performing (3-class) model achieved a mean rest recall of 87% as well as character recall and precision of 65% and 59%, respectively. These results showed that attempted handwriting can be used in an online multi-class selection BCI and that temporal neural features capture the internal user context. Linear warping of trials to minimise temporal variability, where all trials were modified to have the same duration, led to significantly increased decoding performance compared to classifying raw trials. Significance. The results from our participant showed that decoding brain activity based on the differences between feature trajectories across time can be an effective strategy for discriminating different movements. Therefore, BCIs may be able to use temporal information to reduce their spatial footprint, likely making them less invasive. Overall, our study shows that further investigation of temporal decoding across multiple participants and other recording modalities is warranted. Strategies for automatic temporal alignment are likely to further improve the performance of these systems.