Xiaopei Wu

Implementation of the EOG-Based Human Computer Interface System

Bio-based human computer interface(HCI) has attracted more and more attention of researches all over the world in recent years. In this paper, an EOG-based HCI system is introduced. It is composed of three parts: EOG amplifying and acquisition, EOG pattern recognition and control command output. Three plane electrodes are employed to detects the EOG signals, which contains the information related to the eye blinking and vertical( or horizontal ) eye movements referred to pre-designed command table. An online signal processing algorithm is designated to get the command information contained in EOG signals, and these commands could be used to control the computer or other instruments. Based on this HCI system, the remote control experiments driven by EOG are realized.

A novel eye movement detection algorithm for EOG driven human computer interface

Bio-based human computer interface (HCI) has attracted more and more attention of researchers all over the world in recent years. The paper is concerned with eye movement detection algorithm and the EOG-based human computer interface. The linear predictive coding cepstrum (LPCC) coefficients of EOG pulses are extracted as feature vectors, which are used for eye movement pattern matching. Dynamic time warping (DTW) is adopted to solve the discrepancy in EOG pulses duration among different trials. Furthermore, spectral entropy algorithm is used to detect the endpoints of EOG pulses, which can improve the robustness and increase the recognition rate in noisy background. Experimental and simulation results based on real-life EOG signals show that the proposed algorithm has stable performance and can be used for online controlling and communication in EOG based HCI system.

Envelope detection based on online ICA algorithm and its application to motor imagery classification

Independent Component Analysis (ICA) is a promising tool for brain-computer interface (BCI). But most of ICA-based BCI researches only used batch ICA algorithm as the offline preprocessing step for EEG artifact removal and pattern enhancement. This paper explored a new approach of applying online ICA based on sliding window Infomax algorithm for BCI implementation. In addition to having good performance of blind source separation as traditional ICA, the proposed method has the characteristics that can synchronously realize the online envelope detection of multi-channel signals, which is that other methods do not have, such as Hilbert transform. Then the online ICA is applied to the envelope detection of mu rhythm evoked by motor imagery and good classification results of imagining left and right hand movement are achieved on real-life data.

Motor Imagery EEG Classification Based on Dynamic ICA Mixing Matrix

Recently the mu rhythm by motor imagination has been used as a reliable EEG pattern for brain-computer interface (BCI) system. To motor-imagery-based BCI, feature extraction and classification are two critical stages. This paper explores a dynamic ICA base on sliding window Infomax algorithm to analyze motor imagery EEG. The method can get a dynamic mixing matrix with the new data inputting, which is unlike the static mixing matrix in traditional ICA algorithm. And by using the feature patterns based on total energy of dynamic mixing matrix coefficients in a certain time window, the classification accuracy without training can be achieved beyond 85% for BCI competition 2003 data set Ⅲ. The results demonstrate that the method can be used for the extraction and classification of motor imagery EEG. In the present study, it suggests that the proposed algorithm may provide a valuable alternative to study motor imagery EEG for BCI applications.

Low-cost circuit design of EEG signal acquisition for the brain-computer interface system

Increasing number of research activities and different types of studies in brain-computer interface systems (BCIs) show potential in this young research area. However, BCIs have not become widely applied, most of them are still limited in the laboratory and off-line. One of the important reasons is that: EEG signal acquisition is completed by the professional medical equipments. They are expensive and the parameters of them cannot be flexiblely changed with the specific BCI experiment paradigm. In the paper, a single-channel low-cost circuit of EEG signal acquisition for the BCI system is designed. The circuit is composed of protection circuit, instrumentation amplifier, common mode rejection (CMR) circuit, gain adjustable amplifiers and filters. In order to test this circuit, the circuit simulation and the real-time EEG measurements are implemented. The experimental results show that the circuit is effective with good performance, it is very suitable for the online BCI system.

Design and Implementation of a BCI System Based on Steady-State Visual Evoked Potentials

In recent years, Brain-computer interface (BCI) based on electroencephalogram (EEG) is an active topic in brain function research. BCI provides a direct communication and control channel for sending messages and instructions from brain to external computers or other electronic devices. However, the EEG signals are very faint and often corrupted by noises such as power line noise, EMG, EOG etc.. EEG signal acquisition is very critical for the BCI system. In the paper, a circuit for EEG signal acquisition is designed. The circuit module is mainly composed of protection circuit, instrumentation amplifier, common mode rejection (CMR) circuit, gain adjustable amplifiers and filters. Finally, a BCI system based on SSVEP sets up by this hardware circuits platform. The experimental results show that the circuit is effective with good amplification ability and high CMR, and it is very suitable for the BCI system.

Simultaneous detection of blink and heart rate using multi-channel ICA from smart phone videos

Abstract Physiological indexes, such as blink frequency and heart rate, express the physical and mental state of a human. This paper presents an algorithm for simultaneous detection of eye blink and heart rate using multi-channel ICA. Video sequences captured from smart phones are used in which subjects are requested to sit without big motion. Statistic information from the R, G and B components of eyes and their surrounding facial region is explored to discriminate the different sources that mixed in each image. The proposed algorithm extracts both eye blink and cardiac signal as different sources at the same time by 6-channel SOBI without any other complex processing. Meanwhile, a kurtosis based method is proposed to automatically select blink and cardiac signals from the output separations. Different ICA algorithms and channel numbers as well as a series of head moving modes are employed to test the robustness and accuracy of the algorithm. Experiments on twenty subjects show that multi-channel ICA is capable of precisely separating the eye blink and cardiac signals in a less complex way.

A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface

Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The “high quality” training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.

Design and implementation of an eye gesture perception system based on electrooculography

Abstract People with motor diseases have suffered from deprivation of both verbal and non-verbal communication abilities. Fortunately, some of them still retain coordination of brain and eye-motor. To establish a stable communication way for these disabled people, this paper presents an eye gesture perception system based on Electrooculography (EOG). In order to implement a high-accuracy of unit saccadic EOG signals recognition, we propose a new feature extraction algorithm based on Common Spatial Pattern (CSP). We first establish a CSP spatial filter bank corresponding to 8 saccadic tasks (i.e., up, down, left, right, right-up, left-up, right-down, and left-down), then use it to linearly project raw EOG signals and treat the outputs as feature parameters. Furthermore, eye gestures recognition has been carried out by identifying and merging unit saccadic segments in terms of pre-defined time sequences. Experiential results over 10 subjects show that the recognition precision of unit saccadic EOG and eye gesture are 96.8% and 95.0% respectively, which reveal the proposed system has a good performance of eye gestures perception.

Design and Implementation of an Improved BCI System Based on Alpha Rhythm

This paper introduces an improved BCI system based on alpha rhythm, the main composition units of the system are electrodes, acquisition circuit, online detecting algorithm and outer devices. For improving the performance of BCT systems, a new algorithms for alpha wave detection is proposed, which can reduce EEG baseline wandering and energy fluctuation efficiently and make the BCI system work more stable. Based on the BCI system, experiments of media player control are carried out among different subjects, which show that the system has an excellent performance in alpha wave detection and real-time controlling.