Motor Imagery with Brain- Computer Interface Neurotechnology (original) (raw)
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MOTOR IMAGERY BCI SYSTEM WITH VISUAL FEEDBACK: DESIGN AND PRELIMINARY EVALUATION
— Nowadays, strokes are a growing cause of mortality and many people remain with motor sequelae and troubles in the daily activities. To treat these sequelae, alternative rehabilitation techniques are needed. This article describes the design, development and preliminary evaluation of a system based on Brain Computer Interfaces (BCI) by Motor Imagery, with visual feedback for lower limb rehabilitation of people post stroke. The system consists of three modules: Sensing and Conditioning; Control Signal Generator; and Visual Feedback. The first module acquires, filters and segments 5 channels of EEG. The second module performs spatial filtering using a Laplacian, estimates the signal power spectral density, extracts and selects EEG features which are then used by the classifier to detect event related desynchronization. The command signal generated by the BCI is inputted into the third module, which simulates the movement of foot dorsiflexion of an avatar displayed on a screen. For the implementation, the BCI2000, V-REP platforms and MATLAB software were used. Performance evaluation of the system was done in a healthy volunteer by estimating the sensitivity and specificity, and through interviews with specialists. Average values for sensitivity and specificity were 0,67 and 0,70 respectively, and professional opinions were very good. These results are encouraging for deepening the performance evaluation system and taking steps for clinical implementation.
Motor Imagery Learning using a Brain-Computer Interface with Auditory Feedback
2011
Over the last decade massive progress has been made in both brain-computer interface (BCI) and assistive robotics (AR) fields. A BCI primarily utilizes brainwaves modulations generated through voluntary cognitive tasks effected cortical activations. These modulations can be used to establish a direct communication link between human brain and computing devices and may also be effective in plastic reorganisation of neuronal structure after lesion formation due to neurological problems such as stroke. Several promising prototype BCI applications have been reported primarily for providing independence to people with extreme motor disability and helping in the recovery of paralysed limbs of individuals suffering from motor impairments due to stroke. However due to practical limitations, there is still very little take-up of BCI systems for real-world use.
Over 2 million people are affected by neural diseases such as multiple Sclerosis, Amyotrophic Lateral Sclerosis, spinal cord injury, cerebral palsy, and other diseases impairing the neural pathways that control muscles. Indeed, these diseases cause severe paralysis and the persons suffer from what is called "Locked in syndrom". Consequently, a Brain Computer Interface noted BCI can be used as an alternative communication channel. This project belongs to a Brain Computer Interface research. More precisely, it focuses on the development of noninvasive platform of electroencephalographic (EEG) signals in terms of acquisition, pre-processing, feature extraction for providing an alternative communication or control channel for patient with severe motor disabilities.
Neuroscience and Behavioral Physiology, 2018
Neurocomputer interfaces or, as they have come to be known in the Russian literature, brain-computer interfaces (BCI), are used in several areas and have the potential for uses in solving both research and applied tasks. Pilot studies in the clinical application of BCI to poststroke neurorehabilitation are currently under way [Frolov et al., 2013; Ang et al., 2010], and there are prospects for the use of BCI for direct restoration of movement/communication capabilities by creating an alternative information exchange channel with intelligent prostheses and the surroundings. Studies using electrophysiological data generate the need to process multidimensional, nonstationary signals, refl ecting complex physiological processes. Interfaces based on noninvasive technologies for recording brain activity do not as yet provide reliable information links with the user's brain. The results of our studies show that improvements in the working characteristics of these systems can be obtained by constructing new machine learning algorithms considering the physiological and psychoemotional characteristics of BCI use. These algorithms can be developed either in the classical Bayesian paradigm or using state-of-the-art deep learning techniques. In addition, the creation of methods for the physiological interpretation of nonlinear decision rules found by multilayered structures opens up new potentials for the automatic and objective extraction of knowledge from experimental neurophysiological data. Despite the attractiveness of noninvasive technologies, radical increases in the throughput of BCI communication channels and the use of this technology to control prostheses can only be obtained using invasive methods of recording brain activity. Electrocorticograms (ECoG) are the least invasive of these technologies, and in the concluding part of this work we will demonstrate that ECoG can be used for decoding of the kinematic characteristics of fi nger movements.
Motor Imagery based Brain Computer Interface for Windows Operating System
IRJET, 2022
This paper proposes a motor imagery based brain computer interface (MI-BCI) that enables physically challenged individuals to use a computer system that is running Windows operating system. Using the MI-BCI, the user can perform basic operations on Windows such as movement of mouse and execution of mouse clicks. The MI-BCI uses Electroencephalogram (EEG) waves, captured from the user in real time, as input to a convolution neural network (CNN), which classifies the input into one of seven events: mouse movement in left, right up or down direction, left and right mouse click and finally the idle state in which no action is taken. The CNN has five layers including a max pooling layer and a fully connected (FC) layer. The hardware used to capture EEG data is the 8 channel Enobio EEG device with dry electrodes. The training data consists of beta waves (a type of EEG wave) recorded while the subject imagined moving the right arm in one of the four directions; right, left up and down and also executed left and right eye winks which is mapped to left and right mouse clicks in Windows. EEG waves are also captured while the subject is in a idle state (That is, when the subject does not want to move the mouse). The control of the mouse in Windows operating system is achieved using Python libraries. The proposed MI-BCI system is very responsive, and achieved an average accuracy of 92.85% and a highest accuracy of 97.14%.
Motor imagery based EEG features visualization for BCI applications
Procedia Computer Science, 2018
Over recent years, electroencephalography's (EEG) use in the state-of-the-art brain-computer interface (BCI) technology has broadened to augment the quality of life, both with medical and non-medical applications. For medical applications, the availability of real-time data for processing, which could be used as command signals to control robotic devices, is limited to specific platforms. This paper focuses on the possibility to analyse and visualize EEG signal features using OpenViBE acquisition platform in offline mode apart from its default real-time processing capability, and the options available for processing of data in offline mode. We employed OpenViBE platform to acquire EEG signals, pre-process it and extract features for a BCI system. For testing purposes, we analysed and tried to visualize EEG data offline, by developing scenarios, using method for quantification of event-related (de)synchronization ERD/ERS patterns, as well as, built in signal processing algorithms available in OpenViBE-designer toolbox. Acquired data was based on deployment of standard Graz BCI experimental protocol, used for foot kinaesthetic motor imagery (KMI). Results clearly reflect that the platform OpenViBE is a streaming tool that encourages processing and analysis of EEG data online, contrary to analysis, or visualization of data in offline, or global mode. For offline analysis and visualization of data, other relevant platforms are discussed. In online execution of BCI, OpenViBE is a potential tool for the control of wearable lower-limb devices, robotic vehicles and rehabilitation equipment. Other applications include remote control of mechatronic devices, or driving of passenger cars by human thoughts.
Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000
Journal of visualized experiments : JoVE, 2009
A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features t...
The Wadsworth Center brain-computer interface (BCI) research and development program
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003
Center has focused primarily on using electroencephalogram (EEG) rhythms recorded from the scalp over sensorimotor cortex to control cursor movement in one or two dimensions. Recent and current studies seek to improve the speed and accuracy of this control by improving the selection of signal features and their translation into device commands, by incorporating additional signal features, and by optimizing the adaptive interaction between the user and system. In addition, to facilitate the evaluation, comparison, and combination of alternative BCI methods, we have developed a general-purpose BCI system called BCI-2000 and have made it available to other research groups. Finally, in collaboration with several other groups, we are developing simple BCI applications and are testing their practicality and long-term value for people with severe motor disabilities.
Clinical Neurophysiology, 2009
Objective: This study investigates the impact of a continuously presented visual feedback in the form of a grasping hand on the modulation of sensorimotor EEG rhythms during online control of a brain-computer interface (BCI). Methods: Two groups of participants were trained to use left or right hand motor imagery to control a specific output signal on a computer monitor: the experimental group controlled a moving hand performing an object-related grasp ('realistic feedback'), whereas the control group controlled a moving bar ('abstract feedback'). Continuous feedback was realized by using the outcome of a real-time classifier which was based on EEG signals recorded from left and right central sites.