Progress in Brain Computer Interface: Challenges and Opportunities

By S. Saha, K. Mamun, K. Ahmed, R. Mostafa, Ganesh R. Naik, Ahsan Habib Khandoker, S. Darvishi, Mathias Baumert. Frontiers in Systems Neuroscience, 2019

333 citations recorded by the source corpus.

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/edf8b60fb29e6ecc68f10815eab55dabc43a73e3

Citations in the corpus

• Neural representations of visual memory in inferotemporal cortex reveal a generalizable framework for translating between spikes and field potentials (2026, unknown): “…of alignment to HGA compared to other frequency bands and the ease of calculation, further promotes HGA as a promising target control signal in brain machine interfaces and neuromodulatory devices where stability and data efficiency are two important considerations (e.g., reviewed in [64]).”
• Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer (2025, unknown): “• Signal amplitude and SNR: Scalp EEG has low amplitude (10–100 µV) and is prone to artifacts [25], whereas iEEG has higher amplitude (hundreds of µV to several mV) and better SNR due to proximity to neural sources [26].”