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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).
Bayesian Learning for EEG Analysis
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Brain–computer interface (BCI) is a new communication technique that aims at establishing a nonmuscular connection between a human brain and a computer (Chaudhary et al. 2016). A BCI system is developed to translate the intent of a user into computer commands by recognizing a task-related brain activity typically measured using electroencephalography (EEG), for operating external devices, such as wheelchair navigation, character spelling, prosthesis controlling, Internet browsing, and so on (Carlson and Millan 2013; Zhang et al. 2012, 2014a; Chen et al. 2014; Mugler et al. 2010; Yu et al. 2012; Jin et al. 2012). With the help of BCIs, severely disabled patients (e.g., spinal cord injuries, amyotrophic lateral sclerosis, etc.) can potentially recover their environmental control abilities and hence improve their living quality (Leeb et al. 2015; Li and Nam 2016; Klein and Nam 2016).
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).
An assistive technology program for enabling five adolescents emerging from a minimally conscious state to engage in communication, occupation, and leisure opportunities
Published in Developmental Neurorehabilitation, 2022
Fabrizio Stasolla, Alessandro O. Caffò, Sara Bottiroli, Donatella Ciarmoli
Despite the encouraging and promising findings discussed above, the present investigation presented some limitations. First, single-case research designs are traditionally response guided with the researcher making decisions about when to introduce the intervention based on data stability as assessed by visual analysis. Additionally, the multiple baseline design relies on baseline staggers across time rather than number of experimental sessions, to allow for assessment of potential confounds demonstrated by increasing baseline trends,in particular, when the intervention is implemented with the tier above.63 In this study, too few data points for the first participant and the reliance on the number of sessions rather than baseline length with the subsequent difference in baseline lengths small in duration, prevent a high level of confidence about the causal relation between the intervention and the dependent variables. Further, a multiple probe design might be preferable for this population, which is unlikely to show improvement in the absence of intervention and for whom repeated failure during baseline conditions may lead to frustration and resentful demoralization. Second, a systematic replication with different intervention procedures would be interesting. For example, one may envisage the use of wearable technologies. Otherwise, one could examine the use of transcranial deep stimulation and/or brain-computer interface.64,65
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.