Feature domain-specific movement intention detection for stroke rehabilitation with brain-computer interfaces

• Published in: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2016; pp. 5725 - 5728
• Main Authors: Hadsund, J. T., Sorensen, M. B., Royo, A. C., Niazi, I. K., Rovsing, H., Rovsing, C., Jochumsen, M.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-08-2016
• Subjects: Adolescent; Adult; Brain-Computer Interfaces; Complexity theory; Digital filters; Discriminant Analysis; Electroencephalography; Feature extraction; Female; Foot - physiology; Force; Hand - physiology; Humans; Imagination; Induction motors; Intention; Male; Movement; Stroke Rehabilitation - methods; Young Adult
• ISSN: 1557-170X
• DOI: 10.1109/EMBC.2016.7592027

Brain-computer interface (BCI) driven electrical stimulation has been proposed for neuromodulation for stroke rehabilitation by pairing intentions to move with somatosensory feedback from electrical stimulation. Movement intentions have been detected in several studies using different techniques, with temporal and spectral features being the most common. A few studies have compared temporal and spectral features, but conflicting results have been reported. In this study, the aim was to investigate if complexity measures can be used for movement intention detection and to compare the detection performance based on features extracted from three different domains (time, frequency and complexity) from single-trial EEG. Two data sets were used where four different isometric palmar grasps or dorsiflexions were performed while continuous EEG was recorded. 39 healthy subjects performed or imagined these movements and 11 stroke patients attempted to perform the movements. The EEG was pre-processed and divided into two epoch classes: Background EEG (2 s) and movement intention (2 s). To obtain an estimated detection performance, temporal, spectral and complexity features were extracted and classified (linear discriminant analysis) after the feature vector was reduced using sequential forward selection. The results show that accuracies between 82-87% and 74-80% are obtained for foot and hand movements, respectively. The temporal feature domain was the most dominant for foot movement intention detection, while the spectral features contributed more to the hand movement detection. The complexity features could be used to detect movement intentions, but the performance was much lower compared to temporal and spectral features.