Xingwei An

Time-locked and phase-locked features of P300 event-related potentials (ERPs) for brain–computer interface speller

Abstract The brain–computer interface P300 speller is aimed to help those patients unable to activate muscles to spell words by utilizing their brain activity. However, a problem associated with the use of this brain–computer interface paradigm is the generation mechanics of P300 related to responses to visual stimuli. Herein, we investigated the event-related potential (ERP) response for the P300-based brain–computer interface speller. A signal preprocessing method integrated coherent average, principal component analysis (PCA) and independent component analysis (ICA) to reduce the dimensions and noise in the raw data. The time–frequency analysis was based on wavelet and two characteristic parameters of event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) were computed to indicate the evoked response (time-locked) and phase reset (phase-locked) activity, respectively. Results demonstrated that the proposed method was valid for the time-locked and phase-locked feature extraction and both the evoked response and phase reset contributed to the genesis of the P300 signal. These electrophysiological responses characteristics of ERPs would be used for BCI P300 speller design and its signal processing strategies.

A P300-speller based on event-related spectral perturbation (ERSP)

A brain-computer interface (BCI) P300 speller is a novel technique that helps people spell words using the electroencephalography (EEG) without the involvement of muscle activities. However, only time domain ERP features (P300) are used for controlling of the BCI speller. In this paper, we investigated the time-frequency EEG features for the P300-based brain-computer interface speller. A signal preprocessing method integrated ensemble average, principal component analysis, and independent component analysis to remove noise and artifacts in the EEG data. A time-frequency analysis based on wavelet transform was carried out to extract event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) features. Results showed that the proposed signal processing method can effectively extract EEG time-frequency features in the P300 speller, suggesting that ERSP and ITC may be useful for improving the performance of BCI P300 speller.

Digital spelling BCI based on visual-auditory associate stimulation

Brain-computer interfaces (BCI) provide direct and non-muscular communication methods for the people with severe motor impairments. Event-related potentials (ERPs) as efficient modals are commonly used in some of the BCI systems, including visual stimulus, auditory stimulus as well as tactile stimulus. In this experiment, the corresponding Chinese pronunciations were inserted into the visual Oddball series of 1-9 numbers to carry out the cross-sense stimuli of BCI. The experimental data analysis result proves that the P300 components produced by visual-auditory associate stimulation have higher amplitudes and shorter latencies than those produced by visual-only stimulus. For further analysis the constrained independent component analysis (cICA) method was applied when extracting the signal features of ERP and the support vector machine (SVM) method was used to BCI classification. Result proves that the ERPs produced by visual-auditory associate stimulation modal have better recognition efficiency than those in visual-only stimulation. It can relevant the capacity of information alteration in BCI and is worth to do more studies.

Feature selection study of P300 speller using support vector machine

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.

ICA-SVM combination algorithm for identification of motor imagery potentials

Mental tasks such as motor imagery in synchronization with a cue which result event related desynchronization (ERD) and event related synchronization (ERS) are usually studied in brain-computer interface (BCI) system. In this paper we analyze and classify the ERD/ERS response evoked by the motor imagery of left hand, right hand, foot and tongue. The signals were spatially filtered by Independent Component Analysis (ICA) before calculating the power spectral density (PSD) for related electrodes, and then the Support Vector Machine (SVM) was adopted to recognise the different imagery pattern according to ERD/ERS feature for the signals. The results showed that the combination of ICA-based signal extraction algorithm and SVM-based classification method was an effective tool for the identification of motor imagery potentials, with the highest accuracy rate of 91.4% and 77.6% for the lowest.

Neuroprosthesis system for lower limbs action based on functional electrical stimulation

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.

Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency

This paper presented a time-frequency intensity analysis feature extraction approach of lower limb sEMG (Surface Electromyogram) to identify the key gait phases during walking. The proposed feature extraction method used a filter bank of non-linearly scaled wavelets with specified time-resolution to extract time-frequency aspects of the signal.The intensity analysis algorithm was tested on sEMG data collected from ten healthy young volunteers during 30 walking circles for each. Each walking cycle was made up of four key gait phases:L-DS(Left Double Stance), L-SS(Left Single Stance), R-DS(Right Double Stance), R-SS(Right Single Stance).The identification accuracy of 7 subjects using intensity analysis reached 97%, even up to 99.42%.The others were about 95%. The algorithm obviously achieved a higher accuracy of sEMG recognition than the other algorithms such as root mean square and AR Coefficient. In the future, the feature of sEMG signal under different key gait phases may be used in the control of Functional Electrical Stimulation (FES) and other intelligent artificial limbs.

Electroencephalogram mutual information entropy analysis for Alzheimer's disease

To investigate the electroencephalogram (EEG) records in Chinese Han ethnic Alzheimers disease (AD) patients, a nonlinear feature investigation of the resting EEG was carried out on local AD (NINCDS-ADRDA criteria) patients. The age-matched normal elderly subjects served as controls. An estimator of mutual information entropy was introduced to quantify the nonlinear characteristics of time series of the recorded EEG data to address randomness and predictability of brain activities. Results showed that local AD patients exhibited abnormal nonlinear EEG patterns typical of decreased nonlinear dynamic characters with lower mutual information entropies in comparison to the normal, especially in those data collected from the electrode Fp1, Fp2, T3 and T4 (P<0.05). The averaged amplitude of the entropy decreases was more than 20 percent in all these four electrode positions. The progresses in this study may provide some evidence of specific changes of EEG affected by AD in Chinese.

Brain-computer interface technique for electro-acupuncture stimulation control

Electro-acupuncture stimulation (EAS) technique applies the electrical nerve stimulation therapy on traditional acupuncture points to restore the muscle tension. The rapid rise and development of brain-computer interface (BCI) technology makes the thought-control of EAS possible. This paper designed a new BCI-controls-EAS (BCICEAS) system by using event related desynchronization (ERD) of EEG signal evoked by imaginary movement. The Fisher parameters were extracted from feature frequency bands of EEG and classified into EAS control commands by Mahalanobis Classifier. A feedback training technique was introduced to enhance the signal feature through a visual feedback interface with a virtual liquid column, which height varied along with EEG power spectral feature. Experimental results demonstrated the validity of the proposed method, including the effective improvement of feedback training on signal feature and reliable control of EAS. It is hoped the BCICEAS can explore a new way for EAS system design and help people who sufferers with severe movement dysfunction.

Phase resetting and evoked activity contribute to the genesis of P300 signal in BCI system

Brain-computer interface (BCI) is a new human machine interface. Currently, there is a debate about the genesis of the event-related potentials (ERPs). A constituent of the ERPs, the P300, appears to be closely associated with the cognitive processes of the brain. So this research focuses on the genesis of the P300. The event-related spectral perturbation (ERSP) and the inter-trial coherence (ITC) are used in the time-frequency analysis of the signals. The results shows that at the mean values of ERSP and ITC with a P300 signals are much larger than those without a P300 signals, from which we make a conclusion that two models about ERPs both contribute to the genesis of the P300.