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

Abstract

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.

Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain–Computer Interface Application

“…In recent times, future predictions for BCI have motivated researchers to decode the motor activities of non-paralyzed individuals to control the systems without any use of physical manpower [2]. BCI systems acquire signals, i.e., perception, and communicate with the physical environment, i.e., control external devices such as the exoskeleton or wheelchair [3]. The challenging factors in BCI are acquiring signals with high quality, and preprocessing and classifying them to generate commands for the control of external devices [1].…”

Elevating Neuro-Linguistic Decoding: Deepening Neural-Device Interaction with RNN-GRU for Non-Invasive Language Decoding

“…[14].Improving the smooth connection between neural devices and the complex processes of language expression and comprehension is one of the main issues in this field [1]. The need to overcome the drawbacks of the invasive procedures that are typically used in neural interface development is what drives this research [15]. Even though they work well, invasive techniques like implanting electrodes directly into the brain come with risks, such as tissue damage and infections.…”

Exploring the Relationship between Behavioral and Neurological Impairments Due to Mild Cognitive Impairment: Correlation Study between Virtual Kiosk Test and EEG-SSVEP

“…Although MRI allows for the quantitative assessment of brain structure, it could be considered invasive and less suitable for early aMCI screening due to the loud noise [16], potential thermal damage from high-frequency energy [17,18], and the risk of side effects from MRI contrast agents [19]. EEG, on the other hand, can monitor real-time changes in brain activity with temporal accuracy to observe responses to stimuli, albeit with sensitivity to noise [20][21][22][23][24][25]. While various diagnostic tools are employed in clinical settings, each tool presents distinct advantages and limitations.…”