OCULAR ARTIFACTS ELIMINATION AND FEATURE EXTRACTION IN MOTOR IMAGERY-BASED BCI USING NONLINEAR ADAPTIVE FILTER
Gauri Shanker GuptaDusmanta Kumar MohantaSubhojit GhoshGunjan Bhavnesh DaveMaanvi BhatnagarRakesh Kumar Sinha
1
Citation
29
Reference
10
Related Paper
Citation Trend
Abstract:
The paper proposes a novel methodology of de-noising raw electroencephalogram (EEG) data from ocular artifacts (OAs) and alpha waves extraction from motor imagery-based signals that could be further utilized for brain–computer interface (BCI)-based applications. An algorithm based on discrete wavelet transform (DWT) and nonlinear adaptive filtering for the removal of OA is advocated, with an aim of making the process computationally intelligent. This algorithm has been tested on pre-recorded EEG dataset for BCI (Dataset IIIa; obtained from the website of the BCI Competition III). To further validate the competence of the proposed method, synthetic EEG signals were created, which were fused with white Gaussian noise. A total of 20 EEG signals were generated, half of which had added noise with a signal-to-noise ratio (SNR) of 10[Formula: see text]dB and other half had added noise of 5 dBSNR. Each signal contained 1000 samples with a sampling frequency of 250[Formula: see text]Hz. An optimum bandpass filter (FIR and IIR) for extraction of alpha waves has been suggested. FIR Equiripple filter is found most appropriate for the task as it has highest SNR and computes the response faster when compared with other filters. Among different mother wavelets, Daubechies 4 wavelet obtained using statistical thresholding denoises the EEG data most successfully. Correlation and root mean square error (RMSE) parameters show that the performance of nonlinear adaptive filter developed using nonlinear Volterra series has an edge over conventional adaptive filters for the intended purpose.Keywords:
Motor Imagery
Brain Computer Interface(BCI) 기술은 뇌에서 발생한 신호를 직접 해석하여 신체 다른 기관의 표현 없이 대상의 의도를 파악하는 기술을 말한다. 실제 BCI응용의 경우 시각, 청각, 촉각 등 다양한 방법으로 단서(cue)를 제시하고 이를 기반으로 동작 할 수 있는데, 현재 많은 연구들은 BCI알고리즘의 훈련데이터와 평가데이터 사이에서 같은 종류의 단서만을 사용하여 연구를 진행하였다. 본 연구에서는 비침습형 BCI의 대표적인 방식인 EEG 기반 BCI 응용을 시각단서와 청각단서를 이용하여 평가해보았다. 본 연구의 목적은 Neurofeedback이 있는 경우와 없는 경우에 대해서 시각단서와 청각단서에 의한 Motor Imagery를 교차 성능 평가하는 것에 있다. 평가의 대상이 되는 BCI 알고리즘은 Common Spatial Pattern(CSP)과 Least Square Linear Classifier, Linear Discriminant Analysis(LDA), Support Vector Machine(SVM)을 기반으로 왼쪽 또는 오른쪽 팔을 움직이는 운동심상을 분류하며, 임상평가를 통해 실험을 진행하였다.
Motor Imagery
Sensorimotor rhythm
Cite
Citations (0)
Abstract Objective . Brain-computer interface (BCI) systems read and interpret brain activity directly from the brain. They can provide a means of communication or locomotion for patients suffering from neurodegenerative diseases or stroke. However, non-stationarity of brain activity limits the reliable transfer of the algorithms that were trained during a calibration session to real-time BCI control. One source of non-stationarity is the user’s brain response to the BCI output (feedback), for instance, whether the BCI feedback is perceived as an error by the user or not. By taking such sources of non-stationarity into account, the reliability of the BCI can be improved. Approach . In this work, we demonstrate a real-time implementation of a hybrid motor imagery BCI combining the information from the motor imagery signal and the error-related brain activity simultaneously so as to gain benefit from both sources. Main results . We show significantly improved performance in real-time BCI control across 12 participants, compared to a conventional motor imagery BCI. The significant improvement is in terms of classification accuracy, target hit rate, subjective perception of control and information-transfer rate. Moreover, our offline analyses of the recorded EEG data show that the error-related brain activity provides a more reliable source of information than the motor imagery signal. Significance . This work shows, for the first time, that the error-related brain activity classifier compared to the motor imagery classifier is more consistent when trained on calibration data and tested during online control. This likely explains why the proposed hybrid BCI allows for a more reliable means of communication or rehabilitation for patients in need.
Motor Imagery
Interface (matter)
Cite
Citations (18)
This study establishes the effectiveness of event related synchronisation (ERS) features for a system paced brain computer interface (BCI). In particular, the relationship between the duration of motor imagery (MI) and the quality of the features extracted from the ERS is investigated. To this end, two groups of users performed brief (2s) or sustained (4s) MI, and offline single trial BCIs were validated on each group based on features extracted from the EEG before, during and after MI. The BCIs were designed to recognise two intentional control tasks and a no-control state. Cross-validated results indicate that brief MI leads to more informative ERS features than sustained MI.
Motor Imagery
Interface (matter)
Cite
Citations (13)
We studied a motor-imagery brain-computer interface (MI-BCI). An MI-BCI is an interface that allows a computer to be operated by changes in brain activity that occurs when the operator imagines moving a body part. For example, with MI-BCI it is possible to assign left-hand motor-imagery to power an ON/OFF command. One of the problems with MI-BCI is its low performance, especially since MI-BCI has few commands. We aimed to improve the performance of MI-BCI by adding to the number of commands. Currently, MI-BCI has four commands based on “left hand,” “right hand,” “legs,” and “tongue” motor imagery. Therefore, we attempted to add to the number of MI-BCI commands by classifying eight kinds of brain motor-imagery activity: “no imagery,” “left hand,” “right hand,” “legs,” “both hands,” “left hand + legs,” “right hand + legs,” and “both hands + legs.” Motor imagery that involves multiple body parts, for example, “both hands,” is referred to as a multi-mental task. Multi-mental tasks involve a combination of simultaneous motor imagery, for example including the left and right hands and the legs. This makes it possible to increase the number of commands to 2N (where N is the number of body parts). Eighteen healthy males in their twenties participated in this study. The use of multi-mental tasks enabled us to improve MI-BCI performance in two out of three subjects. Multi-mental tasks can be used to add choice to MI tasks. Performance improvements using an MI-BCI were made possible by choosing MI tasks associated with high accuracy.
Motor Imagery
Interface (matter)
Cite
Citations (2)
SUMMARY The brain–computer interface (BCI) is a system to obtain information from brain signals to control computers. P300 and motor imagery tasks in electroencephalograms are the most used features for BCI. However, BCI with P300 classifies only two states and the features of the motor imagery task are too obscure to be classified easily. Therefore, we propose a method of increasing the number of classified states with high accuracy by mixed signal processing for P300 and motor imagery tasks. BCI using P300 and a motor imagery task will have a higher bit rate than conventional BCI. We design an experiment that gives four data classes, namely, control, P300, and P300 for motor imagery of the right hand or left hand. First, we confirm that P300 appears during motor imagery tasks. In addition, we investigate the best method of feature extraction. Finally, we classify four classes by means of multiclass support vector machines, and show the effectiveness of mixed signals that contain P300 and motor imagery.
Motor Imagery
Interface (matter)
Cite
Citations (4)
Motor Imagery
Interface (matter)
Cite
Citations (22)
Motor Imagery
Interface (matter)
Cite
Citations (29)
뇌-컴퓨터 인터페이스 (brain-computer interface; BCI)란 뇌에서 발생한 전기신호를 인공지능 알고리즘을 통해 사용자의 의도를 예측하고, 그에 따라 로봇이나 컴퓨터를 제어해주는 기술로 세계 다양한 기관에서 미래 핵심 기술로 손꼽히는 기술이다. BCI는 구현하는 방법 (Slow Cortical Potentials, Sensorimotor Rhythms, P300, Steady-State Visually Evoked Potential, Directional Tuning 등)에 따라 다양한 어플리케이션에 이용되고 있다. 하지만 BCI를 실생활에 사용하기 위해서는 상황에 따라 시스템을 켜거나 꺼주거나 시스템의 모드 (typing, 로봇 제어, 전동 휠체어 제어 등)를 변경해주어야 한다. 본 논문에서는 일반인 피험자 10명을 대상으로 EEG (Electroencephalography)를 측정 및 분석하여 피험자의 다양한 상태(resting, speech imagery, legs-motor imagery, hands-motor imagery)를 구분해내는 알고리즘을 개발하고 그 결과 88.25%의 정확도로 상태를 구분할 수 있었다. 이는 BCI 모드 변경을 위한 핵심 알고리즘으로 BCI 기술의 실용화를 앞당길 것으로 기대한다.
Motor Imagery
Sensorimotor rhythm
Cite
Citations (2)
Brain-Computer Interface (BCI) is a system to obtain information from the brain signal to control computers. P300 and motor imagery task of Electroencephalogram (EEG) are mainly used features for BCI. However, BCI with P300 classifies only two states and features of motor imagery task are too obscure to be classified easily. Therefore, we propose a method to increase the number of classified states with high accuracy by mixed signal processing for P300 and motor imaginary task. BCI using P300 and motor imaginary task is going to have more bit rate than conventional BCI. We design a experiment which gives 4 classes data as control, P300, and P300 during motor imagery of right or left hand. First, we confirm that P300 appear during motor imagery task. In addition, we examine the best method for feature extraction. Finally, we classify 4 classes by multi-class Support Vector Machines, and show the efficacy of mixed signal which contain P300 and motor imagery.
Motor Imagery
Interface (matter)
SIGNAL (programming language)
Cite
Citations (0)
Multi-class EEG-based BCIs (brain-computer interfaces) usually use a set of different mental tasks to generate different commands. This study shows that, after training with a specially designed BCI paradigm using one motor imagery, humans can learn to predict the time course of band power features of the EEG signals. With this newly-obtained prediction skill, subjects can use only one motor imagery to select one of the four targets on screen
in each trial that lasts 3.4 seconds on average, which is functionally analogous to a 4-class synchronous BCI.
Motor Imagery
Cite
Citations (1)