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Subject independent pattern classification of overt and covert movements from fNIRS signals : Towards neurofeedback training of patients to emulate healthy neural patterns

Several studies have reported on the feasibility of using NIRS for developing brain-computer interface (BCI) devices as an alternate mode of communication and environmental control for the disabled, including its application in neurofeedback training. In the present study, we report the development of a real-time SVM based pattern classification and neurofeedback system using multichannel NIRS. Using this system, we aimed to determine if participants could learn to volitionally regulate motor cortical regions using the help of feedback calculated from the SVM output. As in previous studies, we used left versus right hand movement execution and movement imageries. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject specific models. We obtained high classification accuracies establishing the robustness of the classifier. The third experiment focused on real-time binary classification of movement execution and movement imagery using a subject independent classifier trained on movement execution data from four subjects. The average online classification accuracies for subject independent classification were approximately 63% for movement imagery and 80% for movement execution. We also performed offline analysis to identify the spatial and temporal patterns of activation, and report the classifier parameters. These results have applications towards stroke patients that have improper activation patterns in their motor cortex. This system may be used to train patients to reorganize their brain activity similar to those of healthy individuals. Furthermore, the capability of our system to successfully classify activation patterns from both motor imagery and motor execution supports earlier reports that their underlying neural patterns have common features.