Harnessing Prefrontal Cognitive Signals for Brain-Machine Interfaces.

Trends Biotechnol

Defitech Chair in Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, École Polytechnique Fédérale de Lausanne, 1202, Genève, Switzerland.

Published: July 2017

Brain-machine interfaces (BMIs) enable humans to interact with devices by modulating their brain signals. Despite impressive technological advancements, several obstacles remain. The most commonly used BMI control signals are derived from the brain areas involved in primary sensory- or motor-related processing. However, these signals only reflect a limited range of human intentions. Therefore, additional sources of brain activity for controlling BMIs need to be explored. In particular, higher-order cognitive brain signals, specifically those encoding goal-directed intentions, are natural candidates for enlarging the repertoire of BMI control signals and making them more efficient and intuitive. Thus, here, we identify the prefrontal brain area as a key target region for future BMIs, given its involvement in higher-order, goal-oriented cognitive processes.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.tibtech.2017.03.008DOI Listing

Publication Analysis

Top Keywords

brain-machine interfaces
8
brain signals
8
bmi control
8
control signals
8
signals
6
brain
5
harnessing prefrontal
4
prefrontal cognitive
4
cognitive signals
4
signals brain-machine
4

Similar Publications

Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management.

View Article and Find Full Text PDF

In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence.

View Article and Find Full Text PDF

Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System.

Sensors (Basel)

December 2024

Department of Electronics and Communication Engineering, Istanbul Technical University, 34467 Istanbul, Istanbul, Turkey.

Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain-Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone.

View Article and Find Full Text PDF

The extraction and analysis of pitch underpin speech and music recognition, sound segregation, and other auditory tasks. Perceptually, pitch can be represented as a helix composed of two factors: height monotonically aligns with frequency, while chroma cyclically repeats at doubled frequencies. Although the early perceptual and neurophysiological mechanisms for extracting pitch from acoustic signals have been extensively investigated, the equally essential subsequent stages that bridge to high-level auditory cognition remain less well understood.

View Article and Find Full Text PDF

Cognition relies on transforming sensory inputs into a generalizable understanding of the world. Mirror neurons have been proposed to underlie this process, mapping visual representations of others' actions and sensations onto neurons that mediate our own, providing a conduit for understanding. However, this theory has limitations.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!