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Robotic Technology and Artificial Intelligence in Rehabilitation Medicine
Published in Lawrence S. Chan, William C. Tang, Engineering-Medicine, 2019
Brain computer interface enables severely impaired individuals, such as people with advanced ALS who are not able to use tactile and voice modes of interface, to communicate and access information. This type of interface involves implantation of small electrodes (sensors) into the brain. The sensors pick up the electric impulses from neurons. The signals are translated into commands and transferred to a robotic arm (Schiatti et al. 2017). This allows the individual to pick up a cup of water, feed oneself independently, or move a dot on the screen without moving the limbs. New advancements include improved wireless power, lower power integrated circuits (which generate less heat), better mathematics decoding systems, and improved sensitivity nanoscience electrodes (measures 10,000 neurons).
Design and Customization of SSVEP-Based BCI Applications Aimed for Elderly People
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Piotr Stawicki, Felix Gembler, Ivan Volosyak
Brain–computer interface (BCI) is a field of technologies that allows communication between the human brain and the computer. Brain signal data are scanned for patterns that can be interpreted as control command for external applications (Wolpaw et al. 2002).
Using Brain–Computer Interfaces for Motor Rehabilitation
Published in Stefano Federici, Marcia J. Scherer, Assistive Technology Assessment Handbook, 2017
Giulia Liberati, Stefano Federici, Emanuele Pasqualotto
A brain–computer interface (BCI) is a communication system that provides a direct connection between the brain and an external device, such as a computer or any other system capable of receiving a signal, without relying on the brain's normal output pathways or peripheral nerves and muscles (Figure 16.1) (Birbaumer, 2006; Birbaumer and Cohen, 2007; Wolpaw, Birbaumer, McFarland, Pfurtscheller, and Vaughan, 2002). BCIs can be used by persons with neurodegenerative and motor diseases who have lost motor function, such as those affected by spinal cord injury (Burns, Adeli, and Buford, 2014; Rupp, 2014), cerebral palsy (Cheron et al., 2012; Scherer et al., 2015; Taherian, Selitskiy, Pau, and Claire Davies, 2015), stroke (Curado et al., 2015; Ramos-Murguialday et al., 2013; Silvoni et al., 2011), or amyotrophic lateral sclerosis (ALS) (Halder et al., 2013; Kübler et al., 2005; Nijboer et al., 2008; Riccio, Mattia, Simione, Olivetti, and Cincotti, 2012; Schettini et al., 2015; Silvoni et al., 2016; Simon et al., 2014), to continue communicating and interacting with their environment (Bamdad, Zarshenas, and Auais, 2015).
Portable rehabilitation system with brain-computer interface for inpatients with acute and subacute stroke: A feasibility study
Published in Assistive Technology, 2022
Yasunari Hashimoto, Toshiyuki Kakui, Junichi Ushiba, Meigen Liu, Kyousuke Kamada, Tetsuo Ota
Brain-computer interface (BCI) technology has already been used successfully to control an external device with the user’s brain activity, and it is expected to be used on patients with strokes, spinal cord injuries, and neuromuscular intractable diseases, to assist their motor functions. In addition, the BCIs are investigated on healthy subjects with regard to human augmentation. Recently, several research groups have shown that BCI can also be used as a tool for promoting neural plasticity, leading to functional recovery from hemiplegia/hemiparesis after stroke (Shindo et al., 2011; Ushiba & Soekadar, 2016). The clinical application of such rehabilitative BCI-based neurofeedback in patients with stroke is a fast-growing area of research, and its effectiveness in patients with chronic stroke who have hemiplegia/hemiparesis has recently been confirmed (Broetz et al., 2010; Mukaino et al., 2014).
Mapping the Dimensions of Agency
Published in AJOB Neuroscience, 2021
Andreas Schönau, Ishan Dasgupta, Timothy Brown, Erika Versalovic, Eran Klein, Sara Goering
Most end users of neural technologies are active agents who seek to express themselves—their feelings, emotions, thoughts, and desires—through goal-directed actions. Often, a neural device enables end users to regain abilities lost due to a disease or an injury. A person with Parkinson’s disease, for example, may benefit from a deep brain stimulator (DBS) that alleviates tremor and rigidity, and thus restores the ability to fluently perform movements. A person living with spinal cord injury may benefit from a brain computer interface (BCI) to control a robotic arm, or even to regain a lost sensation of touch. A person with amyotrophic lateral sclerosis (ALS) may use a BCI to communicate with loved ones through the translation of thought to computer-generated speech. A depressed person may use a DBS to improve mood, in the hope of regaining a brighter, more authentic self.
Neuroethical Consciousness
Published in AJOB Neuroscience, 2020
An essential element in the development of successful neurotechnological devices is the first-person perspective of the user. In order for a person to control bodily movement through a brain-computer interface (BCI) a considerable amount of training is required. This training occurs at two levels: first, the BCI is “trained” to detect and decode the neural processes that underlie movement in part through the user’s response to the effected movement; second, the user learns to control movement by consciously thinking about the intended movement (direct BCI) or about an attended stimulus (indirect BCI). Identifying the relevant neural activity and enabling bodily movement is, therefore, a two-way process: accurate detection and decoding requires feedback from the user; the user’s conscious thoughts are translated and, in turn, effect movement.