Feasibility of approaches combining sensor and source features in brain–computer interface

• Published in: Journal of neuroscience methods 2012-02, Vol.204 (1), p.168-178
• Main Authors: Ahn, Minkyu, Hong, Jun Hee, Jun, Sung Chan
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Beamforming; Biofeedback, Psychology - methods; Biofeedback, Psychology - physiology; Brain - physiology; Brain Mapping - methods; Brain–computer interface; Electroencephalography - methods; Feasibility Studies; Female; Humans; Imagination - physiology; Male; Movement - physiology; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Source feature; Source imaging; Systems Integration; Task Performance and Analysis; User-Computer Interface
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2011.11.002

► We proposed combined approaches between sensor and source features in brain–computer interface. ► Combining approach of sensor and source features improved about 3.8% in classification accuracy than sensor features alone. ► Source features were supplementary to sensor features. ► Amount of invisible information in sensor space was significant in the low performance datasets. Brain–computer interface (BCI) provides a new channel for communication between brain and computers through brain signals. Cost-effective EEG provides good temporal resolution, but its spatial resolution is poor and sensor information is blurred by inherent noise. To overcome these issues, spatial filtering and feature extraction techniques have been developed. Source imaging, transformation of sensor signals into the source space through source localizer, has gained attention as a new approach for BCI. It has been reported that the source imaging yields some improvement of BCI performance. However, there exists no thorough investigation on how source imaging information overlaps with, and is complementary to, sensor information. Information (visible information) from the source space may overlap as well as be exclusive to information from the sensor space is hypothesized. Therefore, we can extract more information from the sensor and source spaces if our hypothesis is true, thereby contributing to more accurate BCI systems. In this work, features from each space (sensor or source), and two strategies combining sensor and source features are assessed. The information distribution among the sensor, source, and combined spaces is discussed through a Venn diagram for 18 motor imagery datasets. Additional 5 motor imagery datasets from the BCI Competition III site were examined. The results showed that the addition of source information yielded about 3.8% classification improvement for 18 motor imagery datasets and showed an average accuracy of 75.56% for BCI Competition data. Our proposed approach is promising, and improved performance may be possible with better head model.