Brain–machine interfaces (original) (raw)
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International Journal of Industrial Ergonomics, 2011
The primary aims of this research were to examine (1) mu and beta event-related desynchronization/ synchronization (ERD/ERS) during motor imagery tasks with varying movement duration and (2) the potential impacts of movement duration on ERD/ERS patterns. Motor imagery tasks included brief and continuous imagined hand movements. During an imagery task, participants imagined an indicated movement for 1 s (i.e., brief movement imagery) or 5 s (i.e., continuous movement imagery). The results of the study support (1) that mu and beta ERD/ERS patterns are elicited during imagined hand movements and (2) that movement duration affects ERS and does not affect ERD patterns, during motor movement imagery. Additionally, brief movement imagery had a greater impact on mu and beta ERD; continuous movement imagery had a greater impact on mu and beta ERS. This research will be useful for designing future brainecomputer interfaces as it provides valuable insight into the dynamics of electroencephalographic (EEG) oscillatory changes during motor imagery tasks with varying movement duration. Relevance to industry: : Brainecomputer interfaces (BCIs) have gained considerable interests by both research and industry communities who want to improve the quality of life for those who suffer from severe motor disabilities, such as amyotrophic lateral sclerosis (ALS), brainstem stroke, and cerebral palsy (CP). The results of this study should be applied to EEG-based BCI system design in order to enhance accuracy and classification performance for BCI system control.
Brain-Computer Interfacing [In the Spotlight
IEEE Signal Processing Magazine, 2000
Recently, CNN reported on the future of brain-computer interfaces (BCIs) . Brain-computer interfaces are devices that process a user's brain signals to allow direct communication and interaction with the environment. BCIs bypass the normal neuromuscular output pathways and rely on digital signal processing and machine learning to translate brain signals to action ( ). Historically, BCIs were developed with biomedical applications in mind, such as restoring communication in completely paralyzed individuals and replacing lost motor function. More recent applications have targeted non-disabled individuals by exploring the use of BCIs as a novel input device for entertainment and gaming.
Towards Brain-Computer Interfacing
The primary goal of the Wadsworth Center brain-computer interface (BCI) program is to develop electroencephalographic (EEG) BCI systems that can provide severely disabled individuals with an alternative means of communication and/or control. We have shown that people with or without motor disabilities can learn to control sensorimotor rhythms recorded from the scalp to move a computer cursor in one or two dimensions and we have also used the P300 event-related potential as a control signal to make discrete selections. Overall, our research indicates there are several approaches that may provide alternatives for individuals with severe motor disabilities. We are now evaluating the practicality and effectiveness of a BCI communication system for daily use by such individuals in their homes.
Journal of Neural Engineering, 2012
A brain-machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single neurons. We show that the same approach can be realized using brain activity measured directly from the surface of the human cortex using electrocorticography (ECoG). Five subjects, implanted with ECoG implants for the purpose of epilepsy assessment, took part in our study. Subjects used directionally dependent ECoG signals, recorded during active movements of a single arm, to control a computer cursor in one out of two directions. Significant BMI control was achieved in four out of five subjects with correct directional decoding in 69%-86% of the trials (75% on average). Our results demonstrate the feasibility of an online BMI using decoding of movement direction from human ECoG signals. Thus, to achieve such BMIs, ECoG signals might be used in conjunction with or as an alternative to intracortical neural signals.
Physiological properties of brain-machine interface input signals
Journal of Neurophysiology, 2017
Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance—including movement-related information, longevity, and stability—of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal perfo...
2015
A brain-machine interface (BMI), sometimes called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain-machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions. The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortic al plasticity y of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000
Event-related desynchronization (ERD) of sensori-motor rhythms (SMR) can be used for online brain-machine interface (BMI) control, but yields challenges related to the stability of ERD and feedback strategy to optimize BMI learning. Here, we compared two approaches to this challenge in 20 right-handed healthy subjects (HS, five sessions each, S1-S5) and four stroke patients (SP, 15 sessions each, S1-S15). ERD was recorded from a 275-sensor MEG system. During daily training, motor imagery-induced ERD led to visual and proprioceptive feedback delivered through an orthotic device attached to the subjects' hand and fingers.