Minpeng Xu

EMD-based Feature Extraction from Motor Imaginary EEG of Complex Movements

IFMBE Proceedings, 2013

A hybrid brain-computer interface combining the EEG and NIRS

2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings, 2012

Compared to the conventional brain-computer interface (BCI) system, the hybrid BCI provides a mor... more Compared to the conventional brain-computer interface (BCI) system, the hybrid BCI provides a more efficient way for the communication between the brain and the external device. The Electroencephalography (EEG) signal and the change of oxygenation in the brain are two prevailing approaches used in the BCI. However, single physiological signal couldn't provide enough information for a satisfied BCI. This paper proposes a hybrid BCI system based on the combination of the EEG signal and the cerebral blood oxygen changes measured by the nearinfrared spectroscopy system (NIRS) to detect the state of motor imagery (MI). The result shows that the average recognition rate can achieve above 75.04% and the highest rate 91.11%, which are higher than when only using EEG or NIRS. It suggests that the proposed hybrid BCI system has a good performance in the combination of these two different signals. Further investigation may help develop better BCIs with high accuracy and significant efficiency.

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Feature selection study of P300 speller using support vector machine

2010 IEEE International Conference on Robotics and Biomimetics, 2010

P300 speller is a traditional brain computer interface paradigm and focused by lots of current BC... more P300 speller is a traditional brain computer interface paradigm and focused by lots of current BCI researches. In this paper a support vector machine based recursive feature elimination method was adapted to select the optimal channels for character recognition. The margin distance between target and nontarget stimulus in feature space was evaluated by training SVM classifier and then the features from single channel were eliminated one by one, eventually, channel set provided best recognition performance was left as the optimal set. The results showed that using optimal channel set would achieve a higher recognition correct ratio compared with no channel eliminating. Furthermore the optimal features localized on parietal and occipital areas, on which not only P300 components but VEP components also present a high amplitude waveform. It may suggest that row/column intensification in speller matrix arouses a visual evoked potential and contributes a lot to character identification as well as P300.

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Neuroprosthesis system for lower limbs action based on functional electrical stimulation

2011 International Conference on Electrical and Control Engineering, 2011

This paper proposed a neuroprosthesis system for lower limbs action based on functional electrica... more This paper proposed a neuroprosthesis system for lower limbs action based on functional electrical stimulation (FES) to facilitate patient-responsive ambulation by paralyzed patients with the sequelae of strokes and spinal cord injure. This neuroprosthesis system had four independent channels and seven modules in its hardware including controller, D/A converter, constant-current source, wave shaper, function keys, display device and power supply. To evaluate the system performance to assist standing, knee joint angular velocity were measured during hip stimulation on twelve subjects. Both the basic kinematics indicators of step index and time and knee joint angle under the best step threshold during lower leg stimulation were compared with those during normal walking. Experimental results showed the proposed system was reliable and may be widely used in rehabilitation clinics.

The parallel-BCI speller based on the P300 and SSVEP features

2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 2013

ABSTRACT This paper developed a parallel-BCI speller system, consisting of four simultaneously pr... more ABSTRACT This paper developed a parallel-BCI speller system, consisting of four simultaneously presented sub-spellers, where the sub-speller switching and character selection were simultaneously identified through decoding the dominant frequency of steady-state visual-evoked potential (SSVEP) and the time of P300 occurrence in a parallel mode. Five subjects took part in the offline and online experiments. The canonical correlation analysis (CCA) and stepwise linear discriminant analysis (SWLDA) were jointly used to recognize the target character. Online tests showed that the parallel-BCI speller reached the peak information transfer rate (ITR) of 67.4 bit/min with an average of 48.0 bit/min. The results indicate that the proposed parallel-BCI system can be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improve the BCI spelling performance.

Feature selection study of P300 speller using support vector machine

Robotics and Biomimetics, 2010

P300 speller is a traditional brain computer interface paradigm and focused by lots of current BC... more P300 speller is a traditional brain computer interface paradigm and focused by lots of current BCI researches. In this paper a support vector machine based recursive feature elimination method was adapted to select the optimal channels for character recognition. The margin distance between target and nontarget stimulus in feature space was evaluated by training SVM classifier and then the features from single channel were eliminated one by one, eventually, channel set provided best recognition performance was left as the optimal set. The results showed that using optimal channel set would achieve a higher recognition correct ratio compared with no channel eliminating. Furthermore the optimal features localized on parietal and occipital areas, on which not only P300 components but VEP components also present a high amplitude waveform. It may suggest that row/column intensification in speller matrix arouses a visual evoked potential and contributes a lot to character identification as well as P300.

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Neuroprosthesis system for lower limbs action based on functional electrical stimulation

International Conference on Electrical and Control Engineering, 2011

This paper proposed a neuroprosthesis system for lower limbs action based on functional electrica... more This paper proposed a neuroprosthesis system for lower limbs action based on functional electrical stimulation (FES) to facilitate patient-responsive ambulation by paralyzed patients with the sequelae of strokes and spinal cord injure. This neuroprosthesis system had four independent channels and seven modules in its hardware including controller, D/A converter, constant-current source, wave shaper, function keys, display device and power supply. To evaluate the system performance to assist standing, knee joint angular velocity were measured during hip stimulation on twelve subjects. Both the basic kinematics indicators of step index and time and knee joint angle under the best step threshold during lower leg stimulation were compared with those during normal walking. Experimental results showed the proposed system was reliable and may be widely used in rehabilitation clinics.

Channel Selection Based on Phase Measurement in P300-Based Brain-Computer Interface

PLoS ONE, 2013

Most EEG-based brain-computer interface (BCI) paradigms include specific electrode positions. As ... more Most EEG-based brain-computer interface (BCI) paradigms include specific electrode positions. As the structures and activities of the brain vary with each individual, contributing channels should be chosen based on original records of BCIs. Phase measurement is an important approach in EEG analyses, but seldom used for channel selections. In this paper, the phase locking and concentrating value-based recursive feature elimination approach (PLCV-RFE) is proposed to produce robust-EEG channel selections in a P300 speller. The PLCV-RFE, deriving from the phase resetting mechanism, measures the phase relation between EEGs and ranks channels by the recursive strategy. Data recorded from 32 electrodes on 9 subjects are used to evaluate the proposed method. The results show that the PLCV-RFE substantially reduces channel sets and improves recognition accuracies significantly. Moreover, compared with other state-of-the-art feature selection methods (SSNRSF and SVM-RFE), the PLCV-RFE achieves better performance. Thus the phase measurement is available in the channel selection of BCI and it may be an evidence to indirectly support that phase resetting is at least one reason for ERP generations.

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A visual parallel-BCI speller based on the time–frequency coding strategy

Journal of Neural Engineering, 2014

Objective. Spelling is one of the most important issues in brain-computer interface (BCI) researc... more Objective. Spelling is one of the most important issues in brain-computer interface (BCI) research. This paper is to develop a visual parallel-BCI speller system based on the time-frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode. Approach. The parallel-BCI speller was constituted by four independent P300+SSVEP-B (P300 plus SSVEP blocking) spellers with different flicker frequencies, thereby all characters had a specific time-frequency code. To verify its effectiveness, 11 subjects were involved in the offline and online spellings. A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis and stepwise linear discriminant analysis. Main results. Online spellings showed that the proposed parallel-BCI speller had a high performance, reaching the highest information transfer rate of 67.4 bit min −1 , with an average of 54.0 bit min −1 and 43.0 bit min −1 in the three rounds and five rounds, respectively. Significance. The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.

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A hybrid BCI speller paradigm combining P300 potential and the SSVEP blocking feature

Journal of Neural Engineering, 2013

Objective. Hybrid brain-computer interfaces (BCIs) have been proved to be more effective in menta... more Objective. Hybrid brain-computer interfaces (BCIs) have been proved to be more effective in mental control by combining another channel of physiologic control signals. Among those studies, little attention has been paid to the combined use of a steady-state visual evoked potential (SSVEP) and P300 potential, both providing the fastest and the most reliable EEG based BCIs. In this paper, a novel hybrid BCI speller is developed to elicit P300 potential and SSVEP blocking (SSVEP-B) distinctly and simultaneously with the same target stimulus. Approach. Twelve subjects were involved in the study and every one performed offline spelling twice in succession with two different speller paradigms for comparison: hybrid speller and control P300-speller. Feature analysis was adopted from the view of time domain, frequency domain and spatial distribution; the performances were evaluated by character accuracy and information transfer rate (ITR). Main results. Signal analysis of the hybrid paradigm shows that SSVEPs are an evident EEG component during the nontarget phase but are dismissed and replaced by P300 potentials after target stimuli. The absence of an SSVEP, called SSVEP-B, mostly appearing in channel Oz, presents a sharp distinction between target responses and nontarget responses. The r 2 value of SSVEP-B in channel Oz is comparable to that of P300 in channel Cz. Compared with the control P300-speller, the hybrid speller achieves significantly higher accuracy and ITR with combined features. Significance. The results indicate that the combination of P300 with an SSVEP-B improves target discrimination greatly; the proposed hybrid paradigm is superior to the control paradigm in spelling performance. Thus, our findings provide a new approach to improve BCI performances.

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