Computational Intelligence for Pattern Recognition in EEG Signals

Mutasim, Aunnoy K; Tipu, Rayhan Sardar; Bashar, M. Raihanul; Islam, Kafiul; Amin, M. Ashraful

doi:10.1007/978-3-319-89629-8_11

“…Certain chemicals can be consumed over a prolonged period to affect a person's EEG signal. Moreover, in alcohol or psychotropic drug users, assessment of these abnormalities is often done through a measurement called the event-related potential (ERP) [4]. In the ERP, subjects are given a specific test, and the EEG generated is recorded for analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers proposed various methods for the classification of EEG signals for alcoholics and control using ERP results. Spectral entropy in the gamma subband was employed as a feature of the visual ERP of a multichannel EEG signal [11]. Meanwhile, in another study, the relative wavelet bi-spectrum feature was used, which produced an accuracy of up to 90% [5].…”

Section: Introductionmentioning

confidence: 99%

Grey Level Differences Matrix for Alcoholic EEG Signal Classification

Sri Aprillia,

Rizal,

Geraldy Fauzi

2024

JOIV : Int. J. Inform. Visualization

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Electroencephalogram (EEG) signals can provide information on abnormalities in a person's brain and characterize brain activity. Brain injury or diseases can manifest as brain disorders. Trauma or the use of specific chemicals or medications, such as alcohol, can result in brain damage. Previous research has demonstrated variations in the patterns of EEG signals between alcohol-using and non-drinking people. Various techniques, including wavelet and entropy, have been developed to detect alcoholic EEG using event-related potential (ERP) testing. This work proposes a feature extraction technique based on texture analysis for the classification of alcohol EEG signals because ERP-measured EEG often involves many channels. An NxM image is thought to be equivalent to an EEG signal with N channels and a recording duration of M samples. The NxM matrix is formed by channelizing the N-channel EEG signal in this investigation. Normalization is then used to get a matrix value of 0-255 or an 8-bit image in the following step. Five features are measured in four directions, and the Grey Level Difference Matrix (GLDM) approach is utilized for feature extraction. Using five grey-level difference matrix (GLDM) features and linear discriminant analysis as a classifier, the maximum accuracy was achieved at 73.3%. Image processing can still be used to increase accuracy even though the final product is less accurate than the earlier technique. The suggested approach can still be adjusted to work with biomedical signals or image processing techniques like the Grey Level Co-occurrence Matrix (GLCM).

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“…BCI is a thought-provoking field with a rapid evolution because of its applications based on brain-controlled mechatronics devices (wheelchairs [5][6][7][8], robot arm [9][10], robot hand [11], mobile robots [12], household items [13] and intelligent home [14]) or mind-controlled virtual keyboards [15] or 3D simulations. The working principle underlying a Brain-Computer Interface is consisting of the following main phases: acquisition [16], processing, features extraction [17], and classification of signals related to brain patterns [18] triggered by the execution of specific cognitive tasks [19], [20], followed by the translation of the detected biopotentials and transmission of commands to the controlled applications.…”

Section: Introductionmentioning

confidence: 99%

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

Rușanu¹

2022

Preprint

0

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This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

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“…Another achievement of the research conducted in this paper is related to the development of an independent LabVIEW application aimed for the classification of EEG signals in real-time. Previous related papers are mainly focused on the following stages: data acquisition, processing [24], feature extraction [25][26], training of classification models applied on the acquired data and testing the obtained models on different datasets. The final phase corresponding to applying the trained and tested classification model on data acquired in real-time is rarely presented in the previously published articles [27][28].…”

Section: Introductionmentioning

confidence: 99%

Advanced LabVIEW Applications Aimed for the Acquisition, Processing and Classification of the Electroencephalographic Signals Used in a Brain-Computer Interface System

Oa¹

2021

Preprint

1

0

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This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

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Computational Intelligence for Pattern Recognition in EEG Signals

Cited by 7 publications

References 111 publications

Grey Level Differences Matrix for Alcoholic EEG Signal Classification

Grey Level Differences Matrix for Alcoholic EEG Signal Classification

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

Advanced LabVIEW Applications Aimed for the Acquisition, Processing and Classification of the Electroencephalographic Signals Used in a Brain-Computer Interface System

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