A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis

Abstract

Brain-computer interfaces (BCIs) are often based on the control of sensor motor processes, yet sensor motor processes are impaired in patients suffering from amyotrophic lateral sclerosis (ALS). We devised a new paradigm that targets higher level cognitive processes to transmit information from the user to the BCI. We instructed five ALS patients and eleven healthy subjects to either activate self-referential memories or to focus on processes without mnemonic content, while recording a high density electroencephalogram (EEG). Both tasks are likely to modulate activity in the default mode network (DMN) without involving sensor motor pathways. We find that the two tasks can be distinguished from band power modulations in the theta (3 -- 7 Hz) and alpha-range (8 -- 13 Hz) in fronto-parietal areas, consistent with modulation of neural activity in primary nodes of the DMN. Training a support vector machine (SVM) to discriminate the two tasks on theta- and alpha-power in the precuneus, as estimated by a beam forming procedure, resulted in above chance-level decoding accuracy after only one experimental session. Therefore, the presented work could serve as a basis for a novel tool which allows for simple, reliable communication with patients in late stages of ALS.