RETRACTED ARTICLE: Analysis of brain signal processing and
            real-time EEG signal enhancement

Sharma, Prakash Chandra; Raja, Rohit; Vishwakarma, Santosh K.; Sharma, Sanjeev; Mishra, Pankaj; Kushwah, Vivek Singh

doi:10.1007/s11042-022-12887-z

Cited by 6 publications

(1 citation statement)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brain–computer interfaces (BCIs) based on electroencephalographic (EEG) signals are gaining considerable attention in scientific research and application development [ 1 ] because of technological advances and multidisciplinary studies related to brain signals [ 2 , 3 ]. Many categories of EEG signals can be processed, and countless BCI systems have been developed for ordinary use and clinical applications, for example, in the fields of brain-controlled vehicles [ 4 ], drones [ 5 ], assistive devices [ 6 ], intelligent systems [ 7 ], neurorehabilitation [ 8 ], telemedicine [ 9 ], assistive robots [ 10 ], and wheelchairs [ 11 ], to name but a few.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures

Mwata-Velu,

Zamora,

Vasquez-Gomez

et al. 2024

Sensors

View full text Add to dashboard Cite

This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain–computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).

show abstract