Spatial Detection of Multiple Movement Intentions from SAM-Filtered Single-Trial MEG for a high performance BCI

The objective of this study is to test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single trial magneto-encephalographic (MEG) signals. This study was performed using motor execution and motor imagery movements to achieve a potential high performance Brain-Computer interface (BCI). Seven healthy volunteers, naïve to BCI technology, participated in this study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed as the spatial filter. The four-class classification for natural movement intentions was performed offline; Genetic Algorithm based Mahalanobis Linear Distance (GA-MLD) and direct-decision tree classifier (DTC) techniques were adopted for the classification through 10-fold cross-validation. Through SAM imaging, strong and distinct event related desynchronisation (ERD) associated with sustaining, and event related synchronisation (ERS) patterns associated with ceasing of hand movements were observed in the beta band (15 - 30 Hz). The right and left hand ERD/ERS patterns were observed on the contralateral hemispheres for motor execution and motor imagery sessions. Virtual channels were selected from these cortical areas of high activity to correspond with the motor tasks as per the paradigm of the study. Through a statistical comparison between SAM-filtered virtual channels from single trial MEG signals and basic MEG sensors, it was found that SAM-filtered virtual channels significantly increased the classification accuracy for motor execution (GA-MLD: 96.51 ± 2.43 %) as well as motor imagery sessions (GA-MLD: 89.69 ± 3.34%). Thus, multiple movement intentions can be reliably detected from SAM-based spatially-filtered single trial MEG signals. MEG signals associated with natural motor behavior may be utilized for a reliable high-performance brain-computer interface (BCI) and may reduce long-term training compared with conventional BCI methods using rhythm control. This may prove tremendously helpful for patients suffering from various movement disorders to improve their quality of life

Development of an Electroencephalography-Based Brain-Computer Interface Supporting Two-Dimensional Cursor Control

This study aims to explore whether human intentions to move or cease to move right and left hands can be decoded from spatiotemporal features in non-invasive electroencephalography (EEG) in order to control a discrete two-dimensional cursor movement for a potential multi-dimensional Brain-Computer interface (BCI). Five naïve subjects performed either sustaining or stopping a motor task with time locking to a predefined time window by using motor execution with physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored. The performance of the proposed BCI was evaluated by both offline classification and online two-dimensional cursor control. Event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were observed on the contralateral hemisphere to the hand moved for both motor execution and motor imagery. Feature analysis showed that EEG beta band activity in the contralateral hemisphere over the motor cortex provided the best detection of either sustained or ceased movement of the right or left hand. The offline classification of four motor tasks (sustain or cease to move right or left hand) provided 10-fold cross-validation accuracy as high as 88% for motor execution and 73% for motor imagery. The subjects participating in experiments with physical movement were able to complete the online game with motor execution at the average accuracy of 85.5±4.65%; Subjects participating in motor imagery study also completed the game successfully. The proposed BCI provides a new practical multi-dimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training