Time domain Feature extraction and classification of EEG data for Brain Computer Interface

Geethanjali, P.; Mohan, Y. Krishna; Sen, Jinisha

doi:10.1109/fskd.2012.6234336

Cited by 38 publications

(10 citation statements)

References 9 publications

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“…In addition, it has been established through research that a significant association lies between MWL and EEG features extracted in time and frequency domain. Waveform length, zero crossings, mean absolute values, slope signs changes, etc., features are extracted from EEG in a time domain and further utilized in classification tasks in the domain of brain–computer interfacing [ 23 ]. On the other hand, the Alpha and the Theta wave rhythms of EEG signals, respectively, over the parietal and the frontal regions of brain significantly illustrate the MWL variation of participants [ 24 , 25 ].…”

Section: Background and Related Workmentioning

confidence: 99%

A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

et al. 2020

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Analysis of physiological signals, electroencephalography more specifically, is considered a very promising technique to obtain objective measures for mental workload evaluation, however, it requires a complex apparatus to record, and thus, with poor usability in monitoring in-vehicle drivers’ mental workload. This study proposes a methodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through a real driving experiment and deployed in evaluating drivers’ mental workload. Mutual information of electroencephalography and vehicular signals were used as the prime factor for the fusion of features. In order to assess the reliability of the developed feature set mental workload score prediction, classification and event classification tasks were performed using different machine learning models. Moreover, features extracted from electroencephalography were used to compare the performance. In the prediction of mental workload score, expert-defined scores were used as the target values. For classification tasks, true labels were set from contextual information of the experiment. An extensive evaluation of every prediction tasks was carried out using different validation methods. In predicting the mental workload score from the proposed feature set lowest mean absolute error was 0.09 and for classifying mental workload highest accuracy was 94%. According to the outcome of the study, it can be stated that the novel mutual information based features developed through the proposed approach can be employed to classify and monitor in-vehicle drivers’ mental workload.

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Section: Background and Related Workmentioning

confidence: 99%

A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

et al. 2020

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“…Except for LC, these features have especially been used in EMG analysis studies, including limb movement classification. Geethanjali et al [33] conducted a performance comparison of some of these features using LDA classifier and obtained 67-100% accuracy range for pairwise mental tasks classification. Each feature was extracted using a sliding window similar to MA filter process applied in our calculation of IHAR, so the extraction process was consistent for all features types.…”

Section: Plos Onementioning

confidence: 99%

Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio

2020

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Association between electroencephalography (EEG) and individually personal information is being explored by the scientific community. Though person identification using EEG is an attraction among researchers, the complexity of sensing limits using such technologies in real-world applications. In this research, the challenge has been addressed by reducing the complexity of the brain signal acquisition and analysis processes. This was achieved by reducing the number of electrodes, simplifying the critical task without compromising accuracy. Event-related potentials (ERP), a.k.a. time-locked stimulation, was used to collect data from each subject's head. Following a relaxation period, each subject was visually presented a random four-digit number and then asked to think of it for 10 seconds. Fifteen trials were conducted with each subject with relaxation and visual stimulation phases preceding each mental recall segment. We introduce a novel derived feature, dubbed Inter-Hemispheric Amplitude Ratio (IHAR), which expresses the ratio of amplitudes of laterally corresponding electrode pairs. The feature was extracted after expanding the training set using signal augmentation techniques and tested with several machine learning (ML) algorithms, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN). Most of the ML algorithms showed 100% accuracy with 14 electrodes, and according to our results, perfect accuracy can also be achieved using fewer electrodes. However, AF3, AF4, F7, and F8 electrode combination with kNN classifier which yielded 99.0±0.8% testing accuracy is the best for person identification to maintain both user-friendliness and performance. Surprisingly, the relaxation phase manifested the highest accuracy of the three phases.

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“…Time domain techniques are used to convert signal information that varies with respect to time [ 11 ]. Time domain features are simple and rapid to deploy since it does not necessitate transformation and can extracted directly from prototypical signals.…”

Section: Introductionmentioning

confidence: 99%

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

Bakiya

Anitha²,

Sridevi³

et al. 2022

Behavioural Neurology

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Electromyograms (EMG) are a recorded galvanic action of nerves and muscles which assists in diagnosing the disorders associated with muscles and nerves. The efficient discrimination of abnormal EMG signals, myopathy and amyotrophic lateral sclerosis, engage crucial role in automatic diagnostic assistance tools, since EMG signals are nonstationary signals. Hence, for computer-aided identification of abnormalities, extraction of features, selection of superlative feature subset, and developing an efficient classifier are indispensable. Initially, time domain and Wigner-Ville transformed time-frequency features were extracted from abnormal EMG signals for experiments. The selection of substantial characteristics from time and time-frequency features was performed using bat algorithm. Extensively, deep neural network classifier is modelled for selected feature subset using bat algorithm from extracted time and time-frequency features. The performance of deep neural network exerting selected features from bat algorithm was compared with conventional artificial neural network. Results demonstrate that the deep neural network modelled with layers 2 and 3 ( neurons = 2 and 4) using time domain features is efficient in classifying the abnormalities of EMG signals with an accuracy, sensitivity, and specificity of 100% and also exhibited finer performance. Correspondingly, the developed conventional single layer artificial neural network ( neurons = 7 ) with time domain features has shown an accuracy of 83.3%, sensitivity of 100%, and specificity of 71.42%. The work materializes the significance of conventional and deep neural network using time and time-frequency features in diagnosing the abnormal signals exists in neuromuscular system using efficient classification.

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Time domain Feature extraction and classification of EEG data for Brain Computer Interface

Cited by 38 publications

References 9 publications

A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio

Classification of Myopathy and Amyotrophic Lateral Sclerosis Electromyograms Using Bat Algorithm and Deep Neural Networks

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