Classifications of Motor Imagery Tasks in Brain Computer Interface Using Linear Discriminant Analysis

Aldea, Roxana; Fira, Monica

doi:10.14569/ijarai.2014.030702

Cited by 16 publications

(8 citation statements)

References 6 publications

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“…Bashashati et al [22] performed a comparative study using 14 different BCI configurations finding that the logistic regression algorithm and multi‐layer perceptron classifiers were among the best in all different designs. These results go against to findings in publications in the MI‐based BCI field, where the most recommended and utilised classifiers are ldc [23] or SVM algorithms [20]. The main difference between the above‐mentioned work and the present study is that these results are based on the existence of task‐ specific synchrostates.…”

Section: Discussioncontrasting

confidence: 95%

Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG‐based BCI systems

Santamaría

James

2018

Healthcare Technology Letters

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Phase synchronisation between different neural groups is considered an important source of information to understand the underlying mechanisms of brain cognition. This Letter investigated phase-synchronisation patterns from electroencephalogram (EEG) signals recorded from ten healthy participants performing motor imagery (MI) tasks using schematic emotional faces as stimuli. These phase-synchronised states, named synchrostates, are specific for each cognitive task performed by the user. The maximum and minimum number of occurrence states were selected for each subject and task to extract the connectivity network measures based on graph theory to feed a set of classification algorithms. Two MI tasks were successfully classified with the highest accuracy of 85% with corresponding sensitivity and specificity of 85%. In this work, not only the performance of different supervised learning techniques was studied, as well as the optimal subset of features to obtain the best discrimination rates. The robustness of this classification method for MI tasks indicates the possibility of expanding its use for online classification of the brain–computer interface (BCI) systems.

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Section: Discussioncontrasting

confidence: 95%

Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG‐based BCI systems

Santamaría

James

2018

Healthcare Technology Letters

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“…Then we present a feasibility study of a real-time application that provides MI control of a computer game with VR feedback on each successfully imagined and virtually executed movement of hands or feet. The main contributions of this paper include: (1) superior classification accuracy (offline tests) compared to previous works, (2) demonstrates the feasibility, and (3) very good user acceptance of the system.…”

Section: Introductionmentioning

confidence: 89%

“…In case of binary problems, a band power or power spectrum is also an adequate approach [21,38]. The most preferred classification is the supervised machine learning such as the support vector machine (SVM) and linear discriminant analysis (LDA) [2,44]. In case of binary problems, a simple specified threshold could be an efficient choice [21,38,43].…”

Section: Related Work 21 Classical Machine Learningmentioning

confidence: 99%

Brain Computer Interface for Neuro-rehabilitation With Deep Learning Classification and Virtual Reality Feedback

Karácsony

Hansen

Iversen

et al. 2019

Proceedings of the 10th Augmented Human International Conference 2019

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Though Motor Imagery (MI) stroke rehabilitation effectively promotes neural reorganization, current therapeutic methods are immeasurable and their repetitiveness can be demotivating. In this work, a real-time electroencephalogram (EEG) based MI-BCI (Brain Computer Interface) system with a virtual reality (VR) game as a motivational feedback has been developed for stroke rehabilitation. If the subject successfully hits one of the targets, it explodes and thus providing feedback on a successfully imagined and virtually executed movement of hands or feet. Novel classification algorithms with deep learning (DL) and convolutional neural network (CNN) architecture with a unique trial onset detection technique was used. Our classifiers performed better than the previous architectures on datasets from PhysioNet offline database. It provided fine classification in the real-time game setting using a 0.5 second 16 channel input for the CNN architectures. Ten participants reported the training to be interesting, fun and immersive. "It is a bit weird, because it feels like it would be my hands", was one of the comments from a test person. The VR system induced a slight discomfort and a moderate effort for MI activations was reported. We conclude that MI-BCI-VR systems with classifiers based on DL for real-time game applications should be considered for motivating MI stroke rehabilitation. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI; Virtual reality; Usability testing.

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“…This paper is an attempt to overcome such drawbacks using EEG-based BCI technology. BCI is an interesting field of research and applications [1] that can help in creating a new way of communication for persons with severe disabilities [2]. The field of BCI has witnessed a great interest especially concerning robotic devices control, with particular focus on health applications, where the utilization of BCI to control prosthesis devices is increasing the quality of life for the patients suffering from diseases causing permanent/temporary paralysis or suffering from the loss of the limb [3].…”

Section: Introductionmentioning

confidence: 99%

EEG-based Brain Computer Interface Prosthetic Hand using Raspberry Pi 4

Ali¹,

Popescu²,

Hadăr³

et al. 2021

IJACSA

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Accidents, wars, or different diseases can affect upper limbs in such a manner so their amputation is required, with dramatic effects on people's ability to perform tasks such as grabbing, holding objects, or moving them. In this context, it is necessary to develop solutions to support upper limb amputees to perform daily routine activities. BCI (brain-computer interface) offer the ability to use the neural activity of the brain to communicate or control robots, artificial limbs, or machines without physical movement. This article proposing an electroencephalography (EEG) mind-controlled prosthetic arm. It eliminates the drawbacks like the high price, heaviness, and dependency on the intact nerves related to the myoelectric and other types of prostheses currently in use. The developed prototype is a low-cost 3D-printed prosthetic arm controllable via brain commands using EEG-based BCI technology. It includes a stepper motor controlled by Raspberry Pi 4 to perform actions like open/close movement and holding objects.The project has successfully implemented and achieve the aim to create a prototype of a mind-controlled prosthetic arm system in addition to the necessary experimental tests and calculations regarding torque, force, and the weight that the hand can carry. The paper proves the feasibility of the approach and opens the route for improving the design of the prototype to attach it to the upper-limb amputation stump.

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Classifications of Motor Imagery Tasks in Brain Computer Interface Using Linear Discriminant Analysis

Cited by 16 publications

References 6 publications

Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG‐based BCI systems

Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG‐based BCI systems

Brain Computer Interface for Neuro-rehabilitation With Deep Learning Classification and Virtual Reality Feedback

EEG-based Brain Computer Interface Prosthetic Hand using Raspberry Pi 4

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