Sensor Modalities for Brain-Computer Interface Technology: A Comprehensive Literature Review

• Published in: Neurosurgery Vol. 86; no. 2; pp. E108 - E117
• Main Authors: Martini, Michael L, Oermann, Eric Karl, Opie, Nicholas L, Panov, Fedor, Oxley, Thomas, Yaeger, Kurt
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.02.2020; Copyright by the Congress of Neurological Surgeons; Wolters Kluwer Health, Inc
• Subjects: Amyotrophic lateral sclerosis; Artificial intelligence; Biofeedback training; Data mining; Electrodes; Electroencephalography; Interfaces; Literature reviews; Magnetic resonance imaging; Medical imaging; Neuroimaging; Neurons; Neurosurgery; Sensors; Spectrum analysis; Spinal cord injuries; Technology application; Electrodes; Functional neuroimaging; Sensor modalities; Brain-computer interfaces; Neurofeedback
• ISSN: 0148-396X; 1524-4040
• DOI: 10.1093/neuros/nyz286

Abstract Brain-computer interface (BCI) technology is rapidly developing and changing the paradigm of neurorestoration by linking cortical activity with control of an external effector to provide patients with tangible improvements in their ability to interact with the environment. The sensor component of a BCI circuit dictates the resolution of brain pattern recognition and therefore plays an integral role in the technology. Several sensor modalities are currently in use for BCI applications and are broadly either electrode-based or functional neuroimaging-based. Sensors vary in their inherent spatial and temporal resolutions, as well as in practical aspects such as invasiveness, portability, and maintenance. Hybrid BCI systems with multimodal sensory inputs represent a promising development in the field allowing for complimentary function. Artificial intelligence and deep learning algorithms have been applied to BCI systems to achieve faster and more accurate classifications of sensory input and improve user performance in various tasks. Neurofeedback is an important advancement in the field that has been implemented in several types of BCI systems by showing users a real-time display of their recorded brain activity during a task to facilitate their control over their own cortical activity. In this way, neurofeedback has improved BCI classification and enhanced user control over BCI output. Taken together, BCI systems have progressed significantly in recent years in terms of accuracy, speed, and communication. Understanding the sensory components of a BCI is essential for neurosurgeons and clinicians as they help advance this technology in the clinical setting.