Patterns Identification using Blind Source Separation with Application to Neural Activities Associated with Anticipated Falls

Exploring the nature of neural activity in humans attracts much attention in human-machine interfaces. However, there is still a lack of understanding about the neural activities associated with human anticipated falls. This study examines how the brain responds to anticipated falls by identifying the patterns of neural activities associated with anticipated falls with Electroencephalograms (EEG). We developed a method for pattern identification of neural activities with second-order blind identification in the machine learning framework. The effects of artifact removal within EEG data are compared between the SOBI algorithm and a related method. Experiments involving 135 anticipated falls and 135 walk controls show that the median sample entropy of the EEG signals in the anticipated falls is from 0.1151 to 0.2267 smaller (p-value <0.01) than those in the walk controls. In anticipated falls detection, the average accuracy with random forests is 85.2% for channel O2, which is nearly 10% higher than the channel temporal cerebral cortex (T3 and T4). The results can further guide us in developing new systems of human–machine interfaces, such as EEG data-augmented wearable airbags, to prevent anticipated falls.