EEG-Based Brain Computer Interface for Emotion Recognition

Bano, KM Shahin; Bhuyan, Prachet; Ray, Abhishek

doi:10.1109/cine56307.2022.10037255

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…In addition to acoustic sources of interference, there are also numerous types of electromagnetic fields present in everyday life that may interfere with the proper functioning of a BCI system due to its reliance on electrical signals generated by neurons within the brain [116]. Common sources include power lines, radio waves emitted from cell phones or Wi-Fi routers, etc., all of which could potentially disrupt neural activity recorded via EEG electrodes, thus reducing overall accuracy when detecting specific mental states or commands given by users [117].…”

Section: Noise and Environmental Disturbances Impact On Bci Systemsmentioning

confidence: 99%

State-of-the-Art on Brain-Computer Interface Technology

Pekša

Mamchur

2023

Sensors

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This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.

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Section: Noise and Environmental Disturbances Impact On Bci Systemsmentioning

confidence: 99%

State-of-the-Art on Brain-Computer Interface Technology

Pekša

Mamchur

2023

Sensors

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“…In [ 167 ], a proposal for an EEG-based brain–computer interface (BCI) was presented. It uses a deep learning method that employed EEG signals recorded by the Muse EEG headband for performing emotion recognition tasks.…”

Section: The Pipeline Of Eeg Signal Analysismentioning

confidence: 99%

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

Chaddad

Kateb

et al. 2023

Sensors

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The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.

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“…Despite certain limitations compared to the medicalgrade systems, the Muse Headband has been reported to be effective for ERP research (Krigolson et al, 2017). Additionally this device has found applications in various areas, including predicting task performance (Papakostas et al, 2017), human stress classification (Asif et al, 2019), emotion recognition (Bano et al, 2022), perception of mental stress (Arsalan et al, 2019), and other purposes. Based on these previous reports, we expected the PSBD devices to have somewhat similar advantages and problems in terms of signal quality.…”

Section: Introductionmentioning

confidence: 99%

Resting-state EEG recorded with gel-based vs. consumer dry electrodes: spectral characteristics and across-device correlations

Kleeva,

Ninenko,

Lebedev

2024

Front. Neurosci.

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IntroductionRecordings of electroencephalographic (EEG) rhythms and their analyses have been instrumental in basic neuroscience, clinical diagnostics, and the field of brain-computer interfaces (BCIs). While in the past such measurements have been conducted mostly in laboratory settings, recent advancements in dry electrode technology pave way to a broader range of consumer and medical application because of their greater convenience compared to gel-based electrodes.MethodsHere we conducted resting-state EEG recordings in two groups of healthy participants using three dry-electrode devices, the PSBD Headband, the PSBD Headphones and the Muse Headband, and one standard gel electrode-based system, the NVX. We examined signal quality for various spatial and spectral ranges which are essential for cognitive monitoring and consumer applications.ResultsDistinctive characteristics of signal quality were found, with the PSBD Headband showing sensitivity in low-frequency ranges and replicating the modulations of delta, theta and alpha power corresponding to the eyes-open and eyes-closed conditions, and the NVX system performing well in capturing high-frequency oscillations. The PSBD Headphones were more prone to low-frequency artifacts compared to the PSBD Headband, yet recorded modulations in the alpha power and had a strong alignment with the NVX at the higher EEG frequencies. The Muse Headband had several limitations in signal quality.DiscussionWe suggest that while dry-electrode technology appears to be appropriate for the EEG rhythm-based applications, the potential benefits of these technologies in terms of ease of use and accessibility should be carefully weighed against the capacity of each given system.

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EEG-Based Brain Computer Interface for Emotion Recognition

Cited by 12 publications

References 13 publications

State-of-the-Art on Brain-Computer Interface Technology

State-of-the-Art on Brain-Computer Interface Technology

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

Resting-state EEG recorded with gel-based vs. consumer dry electrodes: spectral characteristics and across-device correlations

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