Abstract
The classification of motor imagery Electroencephalogram (EEG) of the same limb is important for natural control of neuroprosthesis. Due to the close spatial representations on the motor cortex area of the brain, the discrimination of the different motor imagery tasks is challenging. In this paper, phase synchronization information was proposed to classify motor imagery EEG within the same limb. In addition, non-portable was compared with portable EEG acquisition equipment for the purpose of making the brain computer interface (BCI) system more practical. In the non-portable case, the average accuracy of the binary classification and 3-class classification was 60.6% and 42.7%. In the portable case, the average EEG decoding accuracy of 58.5% and 39.9% was achieved for the two and three tasks. Furthermore, in both two cases, different sets of electrode pairs got the similar results. Moreover, we found that the proposed phase information based method was less sensitive to the number of EEG channels and had less performance degradation in portable EEG equipment. These results show it is possible to use phase synchronization information to discriminate different motor imagery tasks within the same limb. Eventually, this will potentially make the control of neuroprosthesis or other rehabilitation device more natural and intuitive.
Highlights
Since the 1970s, the brain-computer interface (BCI) system, especially the non-invasive BCI system, has made significant progress and has been widely used in medical rehabilitation and daily life [1]–[4]
In [6], motor imagery EEG signals based on left hand, right hand, feet and tongue were applied to the BCI system
As can be seen from the figure, three kinds of motor imagery EEG obtained higher average Phase-locking value (PLV) on the C3-FCz and Pz-C3 electrode pairs, which was similar with those achieved in the first set of
Summary
Since the 1970s, the brain-computer interface (BCI) system, especially the non-invasive BCI system, has made significant progress and has been widely used in medical rehabilitation and daily life [1]–[4]. It is well known that the key of the BCI system is classifying EEG signals to obtain corresponding commands to control external devices [5]. The recognition accuracy of EEG signals directly affects the control performance of the BCI system. Feature extraction and classification of EEG signals play an important role in the development of the BCI system. Motor imagery EEG has been widely studied. In [6], motor imagery EEG signals based on left hand, right hand, feet and tongue were applied to the BCI system
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