Abstract
High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application.
Highlights
The emergent brain-computer interface (BCI) technology allows individuals with severe neuromuscular related locomotive disabilities to directly use their brain to operate or communicate with external peripherals and environments (Daly and Wolpaw, 2008; McFarland and Wolpaw, 2011)
The electrophysiological brain patterns used in EEGbased BCI systems are mainly Steady-State Visual Evoked Potentials (SSVEPs) (Chen et al, 2015; Zhang et al, 2015; Zhao et al, 2016; Nakanishi et al, 2018), P300 (Cavrini et al, 2016), sensorimotor rhythms (SMRs) (Yuan and He, 2014; He et al, 2015), and motion-related cortical potential (MRCP, one kind of a slow cortical potential) (Karimi et al, 2017)
The spectral powers estimated by Lomb-Scargle periodogram were more notable than those estimated by Welch or fast Fourier transform (FFT) method for various incomplete signals
Summary
The emergent brain-computer interface (BCI) technology allows individuals with severe neuromuscular related locomotive disabilities to directly use their brain to operate or communicate with external peripherals and environments (Daly and Wolpaw, 2008; McFarland and Wolpaw, 2011). The BCI system provides an alternative interface bridge which can bypass the Incomplete Motor Imagery EEG conventional motor neural pathways and map brain intentions to relative control commands (Ortiz-Rosario and Adeli, 2013). Easy capture, high time resolution and relative cost effectiveness, the EEG signal has been widely adopted for substantial BCI applications, such as remote quadcopter control (Lin and Jiang, 2015), motion rehabilitation (Xu et al, 2011; Zhao et al, 2016), biometric authentication (Palaniappan, 2008), and emotions prediction (Padilla-Buritica et al, 2016). The SMRs-based BCI is more flexible and suitable for practical applications due to the spontaneous EEG signals, which are generated by individuals voluntarily without any external stimuli
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