Brains, Machines, and the Persons in Between

2019 ◽  
Vol 44 (11) ◽  
pp. 1-4
Author(s):  
Christopher M. Reilly ◽  
Keyword(s):  
Health Care ◽  
Power Relations ◽  
Cognitive Enhancement ◽  
Brain Machine Interface ◽  
Therapeutic Applications ◽  
Ethical Implications ◽  
Private Realm ◽  
Machine Interface ◽  
Human Brains ◽  
Great Harm

The ethical implications of brain–machine interface (BMI) are mind-boggling. Connecting a person’s brain to a computer or other machine and successfully transmitting thoughts and instructions bidirectionally has enormous potential for therapeutic applications in health care; but great harm can come from meddling with fragile human brains, succumbing to the temptations of cognitive enhancement, and exposing vulnerable individuals to the power relations formed among owners, generators, users, and sellers of what was once considered the private realm of human thoughts.

A prototype closed-loop brain–machine interface for the study and treatment of pain

2021 ◽  
Author(s):  
Qiaosheng Zhang ◽  
Sile Hu ◽  
Robert Talay ◽  
Zhengdong Xiao ◽  
David Rosenberg ◽  
...  
Keyword(s):  
Closed Loop ◽  
Brain Machine Interface ◽  
Machine Interface

scholarly journals ‘Ilāj bi’l-Tadbīr (regimenal therapy): a core mode of Unani treatment

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Athar Parvez Ansari
Keyword(s):  
Health Care ◽  
Health Care System ◽  
Holistic Approach ◽  
The Body ◽  
Core Mode ◽  
Unani Medicine ◽  
Therapeutic Applications ◽  
Leech Therapy ◽  
Care System

AbstractSince antiquity, the Unani system of medicine has been participating in health care system. Usually, four modes of treatment viz. regimenal therapy, dietotherapy, pharmacotherapy and surgery are applied for the treatment of diseases. Regimenal therapy is an important mode in which the morbid matter present in the body is either dispersed/excreted or its unnecessary production is blocked or its flow is restricted and the diseases are cured by natural healer of the body, consequently bring back the humoural stability. Nearly 30 regimens have been mentioned in classical Unani literature. Commonest regimenal procedures such as fasd (venesection/phlebotomy), hijāma (cupping), ta‘līq al-‘alaq (hirudotherapy/leech therapy), ishāl (purgation), qay’ (emesis), idrār-i-bawl (diuresis), huqna (enema), ta’rīq (diaphoresis), riyādat (exercise), dalk (massage), hammām (bathing), tadhīn (oiling), natūl (irrigation), sakūb (douching/spraying), inkibāb (steam/vapour application), takmīd (fomentation) etc. are usually applied for the management of various ailments. These regimenal procedures are completely based on holistic approach and are potential but needs to be explored scientifically. This review outlines the therapeutic applications of various regimens of regimenal therapy used in Unani medicine.

A Multi-Step Neural Control for Motor Brain-Machine Interface by Reinforcement Learning

2013 ◽  
Vol 461 ◽  
pp. 565-569 ◽  
Author(s):  
Fang Wang ◽  
Kai Xu ◽  
Qiao Sheng Zhang ◽  
Yi Wen Wang ◽  
Xiao Xiang Zheng
Keyword(s):  
Reinforcement Learning ◽  
Neural Activity ◽  
Neural Control ◽  
Brain Machine Interface ◽  
Learning Approach ◽  
Complex Task ◽  
Neural Data ◽  
Neural Spikes ◽  
Performance Improvements ◽  
Machine Interface

Brain-machine interfaces (BMIs) decode cortical neural spikes of paralyzed patients to control external devices for the purpose of movement restoration. Neuroplasticity induced by conducting a relatively complex task within multistep, is helpful to performance improvements of BMI system. Reinforcement learning (RL) allows the BMI system to interact with the environment to learn the task adaptively without a teacher signal, which is more appropriate to the case for paralyzed patients. In this work, we proposed to apply Q(λ)-learning to multistep goal-directed tasks using users neural activity. Neural data were recorded from M1 of a monkey manipulating a joystick in a center-out task. Compared with a supervised learning approach, significant BMI control was achieved with correct directional decoding in 84.2% and 81% of the trials from naïve states. The results demonstrate that the BMI system was able to complete a task by interacting with the environment, indicating that RL-based methods have the potential to develop more natural BMI systems.

EEG-based Brain–Machine Interface induces neurorehabilitative recovery of paretic hand in patients with chronic stroke

2009 ◽  
Vol 65 ◽  
pp. S49
Author(s):  
Kimiko Kawashima ◽  
Keiichiro Shindo ◽  
Junichi Ushiba ◽  
Yutaka Tomita ◽  
Yoshihisa Masakado ◽  
...  
Keyword(s):  
Chronic Stroke ◽  
Brain Machine Interface ◽  
Machine Interface
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