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In electroencephalography, multi-channel electroencephalogram (EEG) signals are usually utilized to improve classification accuracy. However, a large set of EEG channels increases the computational complexity, reduces the real-time performance and causes wearability difficulties. Channel selection methods have been widely investigated to reduce the number of channels with an acceptable loss of accuracy for EEG-based motor-imagery recognition. In this paper, we present a novel algorithm, called Support Vector Machine-Canonical Correlation Analysis-Channel Selection (SVM-CCA-CS). First, the energy features of the wavelet packet subnodes of the motor-imagery EEG signals are extracted. Then the weights of feature groups are calculated as initial channel weights, based on the CCA algorithm. The initial channel weights are further adjusted, according to the contribution of each channel to the classification accuracy via SVM, and the top channels with larger weights are eventually selected. The results show that the average accuracy of all subjects can reach 80.03% by using the first 30 channels with the largest weights from among the total of 118 channels. For the right hand and foot motor-imagery tasks, the generally applicable optimal channels are mostly located in the left hemisphere. Our generally applicable channel observation of the whole brain cortex suggests contralateral control correspondence: for unilateral motor imagery, the optimal channels are concentrated in the contralateral hemisphere. This is consistent with the contralateral control of the body by the human brain: the majority of the human motor and sensory fibers tend to control the contralateral limbs and pass through the midline of the body. Our proposed method provides optimal acquisition and analysis of the positions of EEG signals in specific motor-imagery tasks.

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A joint manifold learning fusion (JMLF) approach is proposed for nonlinear or mixed sensor modalities with large streams of data. The multimodal sensor data are stacked to form joint manifolds, from which the embedded low intrinsic dimensionalities are discovered for moving targets. The intrinsic low dimensionalities are mapped to resolve the target locations. The JMLF framework is tested on digital imaging and remote sensing image generation scenes with mid-wave infrared (WMIR) data augmented with distributed radio-frequency (RF) Doppler data. Eight manifold learning methods are explored to train the system with the neighborhood preserving embedding showing promise for robust target tracking using video-radio-frequency fusion. The JMLF method shows a 93% improved accuracy as compared to a standard target tracking (e.g., Kalman-filter based) approach.