A Review on Deep Learning Approaches for Motor Imagery EEG Signal Classification for Brain–Computer Interface Systems

Nandhini, A.; Sangeetha, J.

doi:10.1007/978-981-19-9819-5_27

Cited by 4 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The imagining of both right and left body movements is combined with lateralized event-related (de)synchronization (ERS or ERD) from beta (13 to 30 Hz) and mu (7 to 13 Hz) frequency bands of EEG signals [13]. These brain action features are generally collected using the Common Spatial Pattern (CSP) technique and serve as input to the ML approach categorizing imagined body actions [14][15][16]. As a result, the method depends on the user for deliberately modulating its brain activity, thereby, the lateralization is identified [17,18].…”

Section: Introductionmentioning

confidence: 99%

Optimal Fuzzy Logic Enabled EEG Motor Imagery Classification for Brain Computer Interface

Yang,

Shankar,

Perumal

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Brain-computer interface BCI) is a technology that assists in straight link among the human brain as well as external devices like computers or robotic systems, without including muscles and peripheral nerves. BCI allows individuals with motor disabilities to manage external devices with the aid of brain signals such as motor imagery detected from electroencephalography (EEG) signals. An EEG Motor Imagery Classification for BCI is a specific application of EEG in which brain signals directly related to motor imagery tasks are analyzed and classified to control external devices or applications, namely robotic systems or computers. In this regard, the study introduces a Jellyfish Optimization with Fuzzy Logic Enabled EEG Motor Imagery Classification for Brain Computer Interface (JFOFL-MICBCI) technique. The JFOFL-MICBCI technique aims to exploit the fuzzy logic system with metaheuristics for classifying EEC motor imagery signals. It initially executes Continuous Wavelet Transform (CWT) for transforming 1D-EEG signals into 2D time-frequency amplitude ones. For feature extraction, the JFOFL-MICBCI technique uses the SqueezeNet method, and its hyperparameters can be adjusted by the employ of the JFO system. The JFOFL-MICBCI method exploits the adaptive neuro-fuzzy inference system (ANFIS) approach for performing the classification process. A comprehensive range of experiments has been accompanied to demonstrate the higher efficiency of the JFOFL-MICBCI technique. The obtained results inferred the better of the JFOFL-MICBCI technique with other recent systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal Fuzzy Logic Enabled EEG Motor Imagery Classification for Brain Computer Interface

Yang,

Shankar,

Perumal

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms

Li,

Shi,

2024

Cogn Neurodyn

View full text Add to dashboard Cite

Review of EEG-Based Biometrics in 5G-IoT: Current Trends and Future Prospects

Beyrouthy,

Mostafa,

Roshdy

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The increasing integration of the Internet of Things (IoT) into daily life has led to significant changes in our social interactions. The advent of innovative IoT solutions, combined with the enhanced capabilities and expanded reach of 5G wireless networks, is altering the way humans interact with machines. Notably, the advancement of edge computing, underpinned by 5G networks within IoT frameworks, has markedly extended human sensory perception and interaction. A key biometric within these IoT applications is electroencephalography (EEG), recognized for its sensitivity, cost-effectiveness, and distinctiveness. Traditionally linked to brain–computer interface (BCI) applications, EEG is now finding applications in a wider array of fields, from neuroscience research to the emerging area of neuromarketing. The primary aim of this article is to offer a comprehensive review of the current challenges and future directions in EEG data acquisition, processing, and classification, with a particular focus on the increasing reliance on data-driven methods in the realm of 5G wireless network-supported EEG-enabled IoT solutions. Additionally, the article presents a case study on EEG-based emotion recognition, exemplifying EEG’s role as a biometric tool in the IoT domain, propelled by 5G technology.

show abstract

A Review on Deep Learning Approaches for Motor Imagery EEG Signal Classification for Brain–Computer Interface Systems

Cited by 4 publications

References 40 publications

Optimal Fuzzy Logic Enabled EEG Motor Imagery Classification for Brain Computer Interface

Optimal Fuzzy Logic Enabled EEG Motor Imagery Classification for Brain Computer Interface

An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms

Review of EEG-Based Biometrics in 5G-IoT: Current Trends and Future Prospects

Contact Info

Product

Resources

About