Evaluation of Machine Learning Algorithms for Classification of EEG Signals

Ramírez-Arias, Francisco Javier; García-Guerrero, E. E.; Tlelo‐Cuautle, Esteban; Colores-Vargas, Juan Miguel; García–Canseco, Eloísa; López-Bonilla, Oscar Roberto; Galindo-Aldana, Gilberto; Inzunza-González, Everardo

doi:10.3390/technologies10040079

Cited by 24 publications

(15 citation statements)

References 88 publications

(104 reference statements)

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“…The EMD method, on the other hand, will represent the four IMF components with the highest frequency, whose size is the same as the input EMG signal. These data (wavelet coefficients and IMF components) are transformed into a reduced feature vector, representing an important step in classification processes [31]- [33]. Since these features characterise the behaviour of EMG signals, their selection is very important.…”

Section: E Feature Extractionmentioning

confidence: 99%

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Gursoy

2024

ELEKTRON ELEKTROTECH

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Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, F-score parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems.

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Section: E Feature Extractionmentioning

confidence: 99%

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Gursoy

2024

ELEKTRON ELEKTROTECH

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“…This device has been widely used in different research related to emotions and the study of pathologies [30][31] [32] . The data obtained directly from the file of every subject were the preprocessed theta (4-8 Hz), alpha (8-12 Hz), low beta (12)(13)(14)(15)(16), high beta (16-25 Hz) and gamma band signals.…”

Section: Acquisition Of Eeg Datamentioning

confidence: 99%

“…For example, in [12] , an algorithm for attention detection during mathematical reasoning is proposed. In addition, in [13] , an analysis of EEG signals is performed using diverse classification techniques, achieving significant results in motion detection. Another relevant contribution is presented in [14] , with the introduction of a new neural network model designed for classification with a limited amount of motor imagery data.…”

mentioning

confidence: 99%

Study of the Length of time Window in Emotion Recognition based on EEG Signals

Jarillo Silva,

Gómez Pérez,

Domínguez Ramírez

2024

Rev. Mex. Ing. Biomed.

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The objective of this research is to present a comparative analysis using various lengths of time windows (TW) during emotion recognition, employing machine learning techniques and the portable wireless sensing device EPOC+. In this study, entropy will be utilized as a feature to evaluate the performance of different classifier models across various TW lengths, based on a dataset of EEG signals extracted from individuals during emotional stimulation. Two types of analyses were conducted: between-subjects and within-subjects. Performance measures such as accuracy, area under the curve, and Cohen's Kappa coefficient were compared among five supervised classifier models: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Decision Trees (DT). The results indicate that, in both analyses, all five models exhibit higher performance in TW ranging from 2 to 15 seconds, with the 10 seconds TW particularly standing out for between-subjects analysis and the 5-second TW for within-subjects; furthermore, TW exceeding 20 seconds are not recommended. These findings provide valuable guidance for selecting TW in EEG signal analysis when studying emotions.

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“…By prioritizing these considerations, we can ensure that these systems are effective, responsible, and ethical in their use [ 77 ]. Accordingly, several requirements can help to ensure the trustworthiness of deep learning technology in BCI applications based on SSVEPs [ 78 – 80 ]. Some of these requirements include the following: Thorough testing and evaluation: deep learning systems should be tested and evaluated extensively to ensure that they are accurate, reliable, and effective.…”

Section: Current Challenges: Trustworthy Deep Learning In Ssvep-based...mentioning

confidence: 99%

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

Albahri

Al-qaysi

Alzubaidi

et al. 2023

International Journal of Telemedicine and Applications

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The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% ( n = 21 / 30 ). The second category, recurrent neural network (RNN), accounts for 10% ( n = 3 / 30 ). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% ( n = 30 ). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% ( n = 1 / 30 ). The literature’s findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

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Evaluation of Machine Learning Algorithms for Classification of EEG Signals

Cited by 24 publications

References 88 publications

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

Study of the Length of time Window in Emotion Recognition based on EEG Signals

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

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