A cognitive brain-computer interface for patients with amyotrophic lateral sclerosis

Prog Brain Res. 2016:228:221-39. doi: 10.1016/bs.pbr.2016.04.022. Epub 2016 Jun 10.

Abstract

Brain-computer interfaces (BCIs) are often based on the control of sensorimotor processes, yet sensorimotor 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 twelve healthy subjects to either activate self-referential memories or to focus on a process without mnemonic content while recording a high-density electroencephalogram (EEG). Both tasks are designed to modulate activity in the default mode network (DMN) without involving sensorimotor pathways. We find that the two tasks can be distinguished after only one experimental session from the average of the combined bandpower modulations in the theta- (4-7Hz) and alpha-range (8-13Hz), with an average accuracy of 62.5% and 60.8% for healthy subjects and ALS patients, respectively. The spatial weights of the decoding algorithm show a preference for the parietal area, consistent with modulation of neural activity in primary nodes of the DMN.

Keywords: ALS; Brain–computer interface; Brain–machine interface; EEG; locked-in.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Alpha Rhythm / physiology
  • Amyotrophic Lateral Sclerosis / rehabilitation*
  • Brain Mapping
  • Brain-Computer Interfaces*
  • Cognition / physiology*
  • Electroencephalography
  • Electromyography
  • Female
  • Follow-Up Studies
  • Humans
  • Male
  • Middle Aged
  • Neurofeedback / instrumentation
  • Neurofeedback / methods*
  • Parietal Lobe / physiology*
  • Principal Component Analysis
  • Theta Rhythm / physiology
  • User-Computer Interface
  • Young Adult