1D Convolutional Neural Network approach to classify voluntary eye blinks in EEG signals for BCI applications

Giudice, Michele Lo; Varone, Giuseppe; Ieracitano, Cosimo; Mammone, Nadia; Bruna, Arcangelo; Tomaselli, Venera; Morabito, Francesco Carlo

doi:10.1109/ijcnn48605.2020.9207195

Cited by 15 publications

(15 citation statements)

References 19 publications

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“…Similarly, Klimesch [8] found that EEG α and θ oscillations reflect cognitive and memory performance, indicating their potential for mental state detection. Regarding the application of ML and DL methods, Giudice et al [23] developed a 1D-CNN model to detect and discriminate between voluntary and involuntary blinking of the eye using EEG data, demonstrating the potential of DL techniques for such tasks. Mattioli et al [24] proposed an approach based on a 10-layer 1D-CNN to classify four motor imagery (MI) and baseline states, which showed promising results in terms of performance.…”

Section: Previous Studies On Eeg-based Mental State Detectionmentioning

confidence: 99%

Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability

Alreshidi,

Bisandu,

Moulitsas

2023

Sensors

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Predicting pilots’ mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenue for detection. However, the interpretability of machine learning and deep learning models, which are often used for such tasks, remains a significant issue. This study aims to address these challenges by developing an interpretable model to detect four mental states—channelised attention, diverted attention, startle/surprise, and normal state—in pilots using EEG data. The methodology involves training a convolutional neural network on power spectral density features of EEG data from 17 pilots. The model’s interpretability is enhanced via the use of SHapley Additive exPlanations values, which identify the top 10 most influential features for each mental state. The results demonstrate high performance in all metrics, with an average accuracy of 96%, a precision of 96%, a recall of 94%, and an F1 score of 95%. An examination of the effects of mental states on EEG frequency bands further elucidates the neural mechanisms underlying these states. The innovative nature of this study lies in its combination of high-performance model development, improved interpretability, and in-depth analysis of the neural correlates of mental states. This approach not only addresses the critical need for effective and interpretable mental state detection in aviation but also contributes to our understanding of the neural underpinnings of these states. This study thus represents a significant advancement in the field of EEG-based mental state detection.

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Section: Previous Studies On Eeg-based Mental State Detectionmentioning

confidence: 99%

Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability

Alreshidi,

Bisandu,

Moulitsas

2023

Sensors

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“…Giudice et al [5] conducted a study on an eye movement device for people with movement disorders. Cheng et al [6] proposed a method to fuse EEG and eye movement data extracted from motor images.…”

Section: Introductionmentioning

confidence: 99%

“…The behaviors incorporated in this study were chosen following a comparative analysis of EEG signals employed in previous BCI research studies. After behavior selection, AI models were chosen for this study, specifically the 1D-CNN utilized by Giudice et al [5], decision tree (DT) by Yavuz et al [15], MS-CNN and ANN (deep neural network (DNN)) by Huang et al [19], and long short-term memory (LSTM) and gated recurrent unit (GRU) models, which can leverage continuity, a characteristic inherent in time series.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Data Continuity on EEG-Based BCI: A Comparative Study of AI Models

Kim,

Han,

Lee

et al. 2024

Preprint

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This study conducted comparative experiments on continuous data for brain–computer interface (BCI) research using various models depending on the data composition. It aimed to compare the classification performance of artificial intelligence (AI) models on data that considered continuity, a characteristic of time series data, and data that did not. Specifically, this study elucidates data continuity as a characteristic of time-series data. Additionally, electroencephalogram (EEG) signals collected from five behavioral paradigms were categorized into six datasets based on continuity considerations. The experiment involved the training of AI models of similar complexity on the classified data to evaluate their classification performances. In this study, a classifier with interesting performance with respect to data continuity is found, and the results are discussed. In BCI research using EEG signals, action and thought classification achieved a high-performance score of 0.9071 in long short-term memory as data continuity decreased. Additionally, when continuity was not considered, deep neural network demonstrated maximum performance with a score of 0.9178. It was observed that data without consideration of continuity performed well in task classification. Therefore, BCI research focusing on behavior and accident classification based on EEG signals is expected to achieve excellent performance by showing various data characteristics through shuffling rather than considering data continuity. In conclusion, this study is expected to contribute to various research directions related to the continuity of time-series data in BCI research. In future research, the classification and case data will be diversified, and other models will be added to conduct performance comparison experiments using the data obtained based on feature extraction optimization.

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“…Some studies implementing these recent methods have been reported. (Agarwal, 2019) proposed an algorithm able to estimate the timestamps of the start and end of the blinks, (Singla, 2011) compared the efficiency of Support Vector Machine (SVM) against Artificial Neural Network (ANN) on detecting eye opening, closing and blinking, reaching an accuracy of 91.9% for the SVM and 89.3% for the ANN in the blink detection experiment, (Chambayil, 2010) created an Artificial Neural Network (ANN) focused on blink detection in EEG signals, obtaining an accuracy of 90.85%, and (Giudice, 2020) proposed the use of an one dimensional (1D) convolutional neural network (CNN) with the objective of classify recorded eye blink EEG data between voluntary and involuntary, obtaining an accuracy of 97.92%.…”

Section: Introductionmentioning

confidence: 99%

EEG Multipurpose Eye Blink Detector using convolutional neural network

Iaquinta

Silva

Júnior³

et al. 2021

RSD

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The electrical signal emitted by the eyes movement produces a very strong artifact on EEG signal due to its close proximity to the sensors and abundance of occurrence. In the context of detecting eye blink artifacts in EEG waveforms for further removal and signal purification, multiple strategies where proposed in the literature. Most commonly applied methods require the use of a large number of electrodes, complex equipment for sampling and processing data. The goal of this work is to create a reliable and user independent algorithm for detecting and removing eye blink in EEG signals using CNN (convolutional neural network). For training and validation, three sets of public EEG data were used. All three sets contain samples obtained while the recruited subjects performed assigned tasks that included blink voluntarily in specific moments, watch a video and read an article. The model used in this study was able to have an embracing understanding of all the features that distinguish a trivial EEG signal from a signal contaminated with eye blink artifacts without being overfitted by specific features that only occurred in the situations when the signals were registered.

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1D Convolutional Neural Network approach to classify voluntary eye blinks in EEG signals for BCI applications

Cited by 15 publications

References 19 publications

Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability

Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability

Exploring the Impact of Data Continuity on EEG-Based BCI: A Comparative Study of AI Models

EEG Multipurpose Eye Blink Detector using convolutional neural network

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