Classification of Common and Uncommon Tones by P300 Feature Extraction and Identification of Accurate P300 Wave by Machine Learning Algorithms

Akhter, Rafia; Lawal, Kehinde; Rahman, Md. Tanvir; Mazumder, Shamim Ahmed

doi:10.14569/ijacsa.2020.0111080

Cited by 4 publications

(4 citation statements)

References 14 publications

(15 reference statements)

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“…Although the study applied deeplearning techniques, the performance of the current study where EROs in the time-frequency domain were used as features in the 'traditional' machine learning approach was much better than in this study where only time-domain features were used. Moreover, in the current study, the preprocessing step that makes the data more suitable for ML (Akhter et al, 2020) was more comprehensive than in the study by Aellen et al (2021).…”

Section: Discussionmentioning

confidence: 85%

“…Among the different features of EEG data used in the literature, ERO in the Time-Frequency domain provides more information about temporal, spectral, and spatial dynamics of cognitive processes (Aliakbaryhosseinabadi, Kamavuako, Jiang, Farina, & Mrachacz-Kersting, 2019). However, various studies showed that ERP features also had promising classification performances for stimuli classification (Parvar et al, 2014;Tjandrasa & Djanali, 2018;Akhter, Lawal, Tanvir, & Ahmed, 2020;Borra & Magosso, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features

Tülay¹

2023

acin

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Unexpected events in the environment elicit the orienting response that protects humans from dangerous situations and there is great importance in identifying these events, especially in aging. The aims of the current study are attempting to find which classification model exhibits the best performance by means of event-related spectral perturbation (ERSP) features based on EEG and to understand which frequency bands, and time windows, contribute most to the classification of external stimuli. The data of 20 healthy elderly participants were included in the study and the 3-Stimulation auditory oddball paradigm was applied to participants. Different classifiers including Support Vector Machine (SVM) with Linear and Polynomial kernels, Linear Discriminant Analysis (LDA), and Naive Bayes were fed by ERSP features obtained from varying frequency bands and time domains. The classification process was fulfilled using custom-written scripts via the FieldTrip Toolbox (version no: 20220104) integrated with the MVPA-light toolbox running under Matlab R2018b. The best performance was obtained by linear SVM which was fed by theta response (4 -8 HZ) in the early time window (0.1 -0.5 s) with 90% accuracy in the case of standard stimuli distinguished from novel stimuli. Delta responses also exhibit distinctive characteristics for standard and novel stimuli by running LDA (87% accuracy) and polynomial SVM (86% accuracy). These findings show that the delta and theta responses have contributed to detecting standard and novel sounds with remarkable performances of SVM and LDA.

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Section: Discussionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features

Tülay¹

2023

acin

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“…It has been established by different studies that ERP gives a maximum positive peak of around 300 ms-600 ms and the peak is higher for target/oddball stimuli compared with standard stimuli. This component is known as the P300 component [7].…”

Section: Introductionmentioning

confidence: 99%

Detection Of Event-Related Potential Artifacts Of Oddball Paradigm By Unsupervised Machine Learning Algorithm

Akhter¹,

Beyette²

2022

Adv. sci. technol. eng. syst. j.

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Electroencephalography (EEG) is one of the most common and benign methods for analyzing and identifying abnormalities in the human brain. EEG is an incessant measure of the activities of the human brain. In contrast, when the measurement of EEG is bounded by time and the EEG is synchronized to an exterior stimulus, is known as Event-Related Potential (ERP). ERP has the capability to perceive and explore the human brain's responses to specific sensitive, cognitive, or motor events in real time with high temporal resolution. Among the various techniques, the oddball paradigm is very famous in EEG studies. In an oddball paradigm experiment, brain responses to frequent and infrequent stimuli are measured. However, the success of ERP research is very much dependent on the analysis of clean data sets and unfortunately, EEG is a combination of both neural and nonneural activities which introduce significant sources of noise that are not related to the brain's response to the external stimulus. These unrelated non-EEG components are acknowledged as artifacts and due to these, the quality of the EEG may damage by decreasing SNR (signal-to-noise ratio). In addition, these artifacts may mislead the actual information in the study. Addressing this problem, the purpose of this research is to introduce a machine learning algorithm (ML) that can screen EEG/ERP data to remove data epochs that are disrupted by artifacts and thus produce a clean data set. Overall, three unsupervised ML algorithms are applied to identify noisy epochs and it is found that the DBScan method performs best with 93.43% accuracy. Finally, the success of this study will allow the ERP study to have a cleaner ERP data set in normal laboratory conditions with less complexity in the ERP studies.

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“…The diagnosis and analysis of schizophrenic patients can be done through the use of electroencephalogram (EEG) signals [9], [10]. The EEG signals have unique characteristics, variability, and dimensionality [11] as they can provide information about the electrical activities of a human brain [12], [13], and also they have the great potential to predict whether a person is a healthy control or schizophrenic [14]- [16]. In the medical field, EEG signals have vast applications like it can be used to detect epilepsy, comma, clinical death, and schizophrenia [17].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Domain Knowledge and Time Series Features for Automated Detection of Schizophrenia from EEG Signals

Hussain¹,

Pirzada²,

Saba³

et al. 2021

IJACSA

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Over the recent years, Schizophrenia has become a serious mental disorder that is affecting almost 21 million people globally. There are different symptoms to recognize schizophrenia from healthy people. It can affect the thinking pattern of the brain. Delusions, hallucinations, and disorganized speech are the common symptoms of Schizophrenia. In this study, we have used electroencephalography (EEG) signals to analyze and diagnose Schizophrenia using machine learning algorithms and found that temporal features performed well as compared to statistical features. EEG signals are the best way to analyze this disorder as they are intimately linked with human thinking patterns and provide information about brain activities. The present work proposes a Machine Learning (ML) model based on Logistic Regression (LR) along with two feature extraction libraries Time Series Feature Extraction Library (TSFEL) and MNE Python toolkit to diagnose Schizophrenia from EEG signals. The results are analyzed based on 5 different sampling techniques. The dataset was cross-validated using leave one subject out cross-validation (LOSOCV) using Scikit learn and achieve greater accuracy, sensitivity, specificity, macro average recall, and macro f1 score on temporal features respectively.

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Classification of Common and Uncommon Tones by P300 Feature Extraction and Identification of Accurate P300 Wave by Machine Learning Algorithms

Cited by 4 publications

References 14 publications

Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features

Detection of Orienting Response to Novel Sounds in Healthy Elderly Subjects: A Machine Learning Approach Using EEG Features

Detection Of Event-Related Potential Artifacts Of Oddball Paradigm By Unsupervised Machine Learning Algorithm

Evaluating Domain Knowledge and Time Series Features for Automated Detection of Schizophrenia from EEG Signals

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