BackgroundMotor imagery can elicit brain oscillations in Rolandic mu rhythm and central beta rhythm, both originating in the sensorimotor cortex. In contrast with simple limb motor imagery, less work was reported about compound limb motor imagery which involves several parts of limbs. The goal of this study was to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery, and discuss the separability of multiple types of mental tasks.MethodsTen subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot) and rest state. Event-related spectral perturbation (ERSP), power spectral entropy (PSE) and spatial distribution coefficient were adopted to analyze these seven EEG patterns. Then three algorithms of modified multi-class common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM).ResultsThe induced event-related desynchronization (ERD) affects more components within both alpha and beta bands resulting in more broad ERD bands at electrode positions C3, Cz and C4 during left/right hand combined with contralateral foot imagery, whose PSE values are significant higher than that of simple limb motor imagery. From the topographical distribution, simultaneous imagination of upper limb and contralateral lower limb certainly contributes to the activation of more areas on cerebral cortex. Classification result shows that multi-class stationary Tikhonov regularized CSP (Multi-sTRCSP) outperforms other two multi-class CSP methods, with the highest accuracy of 84% and mean accuracy of 70%.ConclusionsThe work implies that there exist the separable differences between simple limb motor imagery and compound limb motor imagery, which can be utilized to build a multimodal classification paradigm in motor imagery based brain-computer interface (BCI) systems.

Motor imagery (MI), sharing similar neural representations to motor execution, is regarded as a window to investigate the cognitive motor processes. However, in comparison to simple limb motor imagery, significantly less work has been reported on brain oscillatory patterns induced by compound limb motor imagery which involves several parts of limbs. This study aims to investigate differences of the electroencephalogram (EEG) patterns as well as cognitive process between simple limb motor imagery and compound limb motor imagery. Ten subjects participated in the experiment involving three tasks of simple limb motor imagery (left hand, right hand, feet) and three tasks of compound limb motor imagery (both hands, left hand combined with right foot, right hand combined with left foot). Simultaneous imagination of different limbs contributes to the activation of larger cortical areas as well as two estimated sources located at corresponding motor areas within beta rhythm. Compared with simple limb motor imagery, compound limb motor imagery presents a network with more effective interactions overlying larger brain regions, additionally shows significantly larger causal flow over sensorimotor areas and larger causal density over both sensorimotor areas and neighboring regions. On the other hand, compound limb motor imagery also shows significantly larger 10–11 Hz alpha desynchronization at occipital areas and central theta synchronization. Furthermore, the phase-locking value (PLV) between central and occipital areas of left/right hand combined with contralateral foot imagery is significantly larger than that of simple limb motor imagery. All these findings imply that there exist apparent intrinsic distinctions of neural mechanism between simple and compound limb motor imagery, which presents a more complex effective connectivity network and may involve a more complex cognitive process during information processing.

The results in this work validate the feasibility of our proposed approach to form a novel MI-SSSEP hybrid BCI outperforming a conventional MI-based BCI through combing MI with electrical stimulation.