The Classification of EEG Signal Using Different Machine Learning Techniques for BCI Application

Rashid, Mamunur; Sulaiman, Norizam; Mustafa, Mahfuzah; Khatun, Sabira; Bari, Bifta Sama

doi:10.1007/978-981-13-7780-8_17

Cited by 23 publications

(15 citation statements)

References 32 publications

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“…The accuracy was around 98% driven by implementing segmentation to the complete signal waveform [ 29 ]. Even though classification could be applied using other EEG features like the average spectral centroid, average standard deviation, or average energy entropy, but still the power spectral density offers the highest accuracy with all classifiers and was found to score 100% with KNN when analyzing EEG signals from different human cognitive states employed to control brain computer interface (BCI) devices [ 30 ]. In contrast to our work which investigated the effect of a single bout of acute exercise, the effect of increasing running exercise intensities on spontaneous EEG was investigated by a study, which found that the overall spectrum power in EEG significantly increased in all frequency bands with increasing intensities of exercise, lactate level has increased, and even after a period of 15- to 30-minute recovery, lactate enzyme level has decreased but still significantly higher than baseline and discernible [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Classification of Lactate Level Using Resting-State EEG Measurements

Shaban

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Duru

2021

Applied Bionics and Biomechanics

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The electroencephalography (EEG) signals have been used widely for studying the brain neural information dynamics and behaviors along with the developing impact of using the machine and deep learning techniques. This work proposes a system based on the fast Fourier transform (FFT) as a feature extraction method for the classification of human brain resting-state electroencephalography (EEG) recorded signals. In the proposed system, the FFT method is applied on the resting-state EEG recordings and the corresponding band powers were calculated. The extracted relative power features are supplied to the classification methods (classifiers) as an input for the classification purpose as a measure of human tiredness through predicting lactate enzyme level, high or low. To validate the suggested method, we used an EEG dataset which has been recorded from a group of elite-level athletes consisting of two classes: not tired, the EEG signals were recorded during the resting-state task before performing acute exercise and tired, the EEG signals were recorded in the resting-state after performing an acute exercise. The performance of three different classifiers was evaluated with two performance measures, accuracy and precision values. The accuracy was achieved above 98% by the K-nearest neighbor (KNN) classifier. The findings of this study indicated that the feature extraction scheme has the ability to classify the analyzed EEG signals accurately and predict the level of lactate enzyme high or low. Many studying fields, like the Internet of Things (IoT) and the brain computer interface (BCI), can utilize the findings of the proposed system in many crucial decision-making applications.

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Section: Discussionmentioning

confidence: 99%

Classification of Lactate Level Using Resting-State EEG Measurements

Shaban

Uçan

Duru

2021

Applied Bionics and Biomechanics

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“…In the present day, the fast growth of machine learning and deep learning approaches enable the investigation of biological signals which happens to be a notable research topic. Professionals have been exploring to figure out and categorize the distinctive biological signals for medical and non-medical application [1,2]. Invasive and non-invasive strategies are used to capture the biological signals from brain of the human beings.…”

Section: Introductionmentioning

confidence: 99%

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

et al. 2020

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Brain-computer interface (BCI) is an important alternative for disabled people that enables the innovative communication pathway among individual thoughts and different assistive appliances. In order to make an efficient BCI system, different physiological signals from the brain have been utilized for instances, steady-state visual evoked potential, motor imagery, P300, movement-related potential and error-related potential. Among these physiological signals, motor imagery is widely used in almost all BCI applications. In this paper, Electrocorticography (ECoG) based motor imagery signal has been classified using long short-term memory (LSTM). ECoG based motor imagery data has been taken from BCI competition III, dataset I. The proposed LSTM approach has achieved the classification accuracy of 99.64%, which is the utmost accuracy in comparison with other state-of-art methods that have employed the same data set.

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“…In a BCI system, specific patterns of brain activity are translated into control commands in the purpose of particular devices operation [2]. Mind-controlled wheelchair [3], home appliances [4], prosthetic arm controlling [5], spelling system [6], emotion detection system [7] and biometrics [8] are the popular BCI applications [9]. Currently, BCI applications have been widened from medical to non-medical fields, for example, BCI based games and virtual reality [9].…”

Section: Introductionmentioning

confidence: 99%

“…Mind-controlled wheelchair [3], home appliances [4], prosthetic arm controlling [5], spelling system [6], emotion detection system [7] and biometrics [8] are the popular BCI applications [9]. Currently, BCI applications have been widened from medical to non-medical fields, for example, BCI based games and virtual reality [9]. Both non-invasive and invasive brain activity recording modalities are contributing progressively in neuroscience research as well as in brain-computer interfacing [10].…”

Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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Facial expression may establish communication between physically disabled people and assistive devices. Different types of facial expression including eye wink, smile, eye blink, looking up and looking down can be extracted from the brain signal. In this study, the possibility of controlling assistive devices using the individual's wink has been investigated. Brain signals from the five subjects have been captured to recognize the left wink, right wink, and no wink. The brain signals have been captured using Emotiv Insight which consists of five channels. Fast Fourier transform and the sample range have been computed to extract the features. The extracted features have been classified with the help of different machine learning algorithms. Here, support vector machine (SVM), linear discriminant analysis (LDA) and K-nearest neighbor (K-NN) have been employed to classify the features sets. The performance of the classifier in terms of accuracy, confusion matrix, true positive and false positive rate and the area under curve (AUC)-receiver operating characteristics (ROC) have been evaluated. In the case of sample range, the highest training and testing accuracies are 98.9% and 96.7% respectively which have been achieved by two classifiers namely, SVM and K-NN. The achieved results indicate that the person's wink can be utilized in controlling assistive devices.

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The Classification of EEG Signal Using Different Machine Learning Techniques for BCI Application

Cited by 23 publications

References 32 publications

Classification of Lactate Level Using Resting-State EEG Measurements

Classification of Lactate Level Using Resting-State EEG Measurements

Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

Wink based facial expression classification using machine learning approach

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