Abdullah Al Shiam

Sheikh Hasina University · Computer Science and Engineering

The major challenge in Brain Computer Interface (BCI) is to obtain reliable classification accuracy of motor imagery (MI) task. This paper mainly focuses on unsupervised feature selection for electroencephalography (EEG) classification leading to BCI implementation. The multichannel EEG signal is decomposed into a number of subband signals. The fea...

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish commu- nication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However,...

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, t...

Machine translation (MT) is the process of translating text from one language to another using bilingual data sets and grammatical rules. Recent works in the field of MT have popularized sequence-to-sequence models leveraging neural attention and deep learning. The success of neural attention models is yet to be construed into a robust framework fo...

Achieving a reliable classification of motor imagery (MI) tasks is a major challenge in brain–computer interface (BCI) implementation. The set of relevant and discriminative features plays an important role in the classification scheme. This paper presents a supervised approach to select discriminative features for the enhancement of MI classificat...

The major challenge in Brain Computer Interface (BCI) is to obtain reliable classification accuracy of motor imagery (MI) task. This paper mainly focuses on unsupervised feature selection for electroencephalography (EEG) classification leading to BCI implementation. The multichannel EEG signal is decomposed into a number of subband signals. The fea...

Canonical correlation analysis (CCA) is commonly used to recognize the frequency of steady state visual evoked potential (SSVEP) for the implementation of brain computer interface (BCI). The performance of CCA is degraded when lower data length is used. On the other hand, BCI implementation becomes more effective when it uses lower data length i.e....