OBJECTIVE: As for stroke rehabilitation, brain-computer interfaces could potentially be used for inducing neural plasticity in patients with cerebral palsy by pairing movement intentions with relevant somatosensory feedback. Therefore, the aim of this study was to investigate if movement intentions from children with cerebral palsy can be detected from single-trial EEG. Moreover, different feature types and electrode setups were evaluated. APPROACH: Eight adolescents with cerebral palsy performed self-paced dorsiflexions of the ankle while nine channels of EEG were recorded. The EEG was divided into movement intention epochs and idle epochs. The data were pre-processed and temporal, spectral and template matching features were extracted and classified using a random forest classifier. The classification accuracy of the 2-class problem was used as an estimation of the detection performance. This analysis was repeated using a single EEG channel, a large Laplacian filtered channel and nine channels. MAIN RESULTS: A classification accuracy of ~70% was obtained using only a single channel. This increased to ~80% for the Laplacian filtered data, while ~75% of the data were correctly classified when using nine channels. In general, the highest accuracies were obtained using temporal features or using all of them combined. SIGNIFICANCE: The results indicate that it is possible to detect movement intentions in patients with cerebral palsy; this may be used in the development of a brain-computer interface for motor rehabilitation of patients with cerebral palsy.
OBJECTIVE: As for stroke rehabilitation, brain-computer interfaces could potentially be used for inducing neural plasticity in patients with cerebral palsy by pairing movement intentions with relevant somatosensory feedback. Therefore, the aim of this study was to investigate if movement intentions from children with cerebral palsy can be detected from single-trial EEG. Moreover, different feature types and electrode setups were evaluated. APPROACH: Eight adolescents with cerebral palsy performed self-paced dorsiflexions of the ankle while nine channels of EEG were recorded. The EEG was divided into movement intention epochs and idle epochs. The data were pre-processed and temporal, spectral and template matching features were extracted and classified using a random forest classifier. The classification accuracy of the 2-class problem was used as an estimation of the detection performance. This analysis was repeated using a single EEG channel, a large Laplacian filtered channel and nine channels. MAIN RESULTS: A classification accuracy of ~70% was obtained using only a single channel. This increased to ~80% for the Laplacian filtered data, while ~75% of the data were correctly classified when using nine channels. In general, the highest accuracies were obtained using temporal features or using all of them combined. SIGNIFICANCE: The results indicate that it is possible to detect movement intentions in patients with cerebral palsy; this may be used in the development of a brain-computer interface for motor rehabilitation of patients with cerebral palsy.