Eye-Blink artifact Detection and Removal Approaches for BCI using EEG

Wankhade, Megha M.; Chorage, S. S.

doi:10.1109/rteict52294.2021.9574024

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Physiological noises can arise due to breathing activity or heartbeats. Although these noises are unavoidable, many methods have been reported to counter these noises; commonly used techniques apply bandpass filters, parameter mapping, and independent component analysis (Rejer and Cieszyński, 2019 ; Vourvopoulos et al, 2019 ; Wankhade and Chorage, 2021 ). Denoising the data further removes data regions; thus, the processed data are even smaller in magnitude than the raw data.…”

Section: Introductionmentioning

confidence: 99%

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

et al. 2022

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The constantly evolving human–machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain–computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.

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Section: Introductionmentioning

confidence: 99%

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

et al. 2022

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“…Brain-computer interface (BCI) technology has witnessed rapid development, injecting new vitality into the fields of neuroscience and engineering [1,2]. Among various BCI applications, the interpretation of electroencephalogram (EEG) signals through Motor Imagery (MI) has emerged as a notable research focus [3][4][5][6][7][8][9][10][11][12]. MI-EEG technology enables individuals to control external devices through brain activity, holding tremendous promise in neuroscience, medical rehabilitation, and intelligent assistive devices.…”

Section: Introductionmentioning

confidence: 99%

A Stepwise Discriminant Analysis and FBCSP Feature Selection Strategy for EEG MI Recognition

Meng,

Su,

et al. 2024

ijacsa

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Accurate decoding of brain intentions is a pivotal technology within Brain-Computer Interface (BCI) systems that rely on Motor Imagery (MI). The effective extraction of information features plays a critical role in the precise decoding of these brain intentions. However, there exists significant individual and environmental variability in signals, and the sensitivity of EEG signals from different subjects also varies, imposing higher demands on both feature exploration and accurate decoding. To address these challenges, we employ adaptive sliding time windows and a stepwise discriminant analysis strategy to selectively extract features obtained through the Filter Bank Common Spatial Pattern (FBCSP). This entails the identification of an optimal feature combination tailored to specific patients, thereby mitigating individual differences and environmental variations. Initially, adaptive sliding time windows are applied to segment electroencephalogram (EEG) data for different subjects, followed by FBCSP for feature extraction. Subsequently, a stepwise discriminant analysis (SDA) incorporating prior knowledge is employed for optimal feature selection, effectively and adaptively identifying the best feature combination for specific subjects. The proposed method is evaluated using two publicly available datasets, the EEG recognition accuracy for Dataset A is 98.47%, and for Dataset B, it is 95.2%. In comparison to current publicly reported research results (utilizing Power Spectral Density (PSD) + Support Vector Machine (SVM) methods) for Dataset A, the proposed method improves MI recognition accuracy by 25.37%. For Dataset B, compared to current publicly reported results (FBCNet method), the proposed method improves MI recognition accuracy by 26.4%. The experimental results underscore the method's broad applicability, scalability, and substantial value for promotion and application.

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“…However, various artifacts can be present in EEG signals, such as Electrocardiogram (ECG), Electromyography (EMG), and eye movement artifacts. Therefore, pre-processing of raw brain signals, extraction of significant features, and classification play a crucial role in the performance of the BMI system [3]. EEG headsets integrated with the ThinkGear chip facilitate signal processing and send the collected data to an open network socket due to the chip [5].…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Human-Computer Interaction through Brain-Machine Interface: An EEG-Based Approach

Patole¹,

Sinha²,

Khairnar³

et al. 2023

IJRASET

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Human-Machine Interface (HMI) devices, such as keyboards and mice, have been the primary means of input for computers since their inception. However, these devices have limitations that make them unsuitable for individuals with motor disabilities, motor impairment, or diseases such as paralysis, muscular dystrophy, polio, cerebral palsy, and others. This limits their ability to fully engage in computer-related activities, which can have a significant impact on their quality of life. This research paper proposes that new and emerging technologies, such as Brain-Machine Interface (BMI) and Machine Learning (ML), could be utilized to design a more convenient and accessible HMI solution that improves the quality of life for individuals with physical disabilities. BMI technology enables communication between the brain and external devices, while ML can analyse data and make predictions based on patterns and models. Combining these technologies can provide a more intuitive and adaptive interface that can detect and respond to the user's intentions and needs

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Eye-Blink artifact Detection and Removal Approaches for BCI using EEG

Cited by 3 publications

References 10 publications

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

A Stepwise Discriminant Analysis and FBCSP Feature Selection Strategy for EEG MI Recognition

Revolutionizing Human-Computer Interaction through Brain-Machine Interface: An EEG-Based Approach

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