A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations

Al-qaysi, Z.T.; Zaidan, B. B.; Zaidan, A. A.; Suzani, M. S.

doi:10.1016/j.cmpb.2018.06.012

Cited by 113 publications

(57 citation statements)

References 27 publications

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“…Therefore, the current offline approach shows great potential to be applied in a realtime BCI system and paves the way for more research and development on the urgent topic of fully automated EEG-controlled wheelchair systems [38]. Additionally, the use of solely EEG signals and not EOG or a combination of EEG and EOG signals is an advantage of this method.…”

Section: Discussionmentioning

confidence: 99%

EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests

Antoniou

Bozios

Christou

et al. 2021

Sensors

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Discrimination of eye movements and visual states is a flourishing field of research and there is an urgent need for non-manual EEG-based wheelchair control and navigation systems. This paper presents a novel system that utilizes a brain–computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while eye movement and subsequently classify them into six categories by applying a random forests (RF) classification algorithm. RF is an ensemble learning method that constructs a series of decision trees where each tree gives a class prediction, and the class with the highest number of class predictions becomes the model’s prediction. The categories of the proposed random forests brain–computer interface (RF-BCI) are defined according to the position of the subject’s eyes: open, closed, left, right, up, and down. The purpose of RF-BCI is to be utilized as an EEG-based control system for driving an electromechanical wheelchair (rehabilitation device). The proposed approach has been tested using a dataset containing 219 records taken from 10 different patients. The BCI implemented the EPOC Flex head cap system, which includes 32 saline felt sensors for capturing the subjects’ EEG signals. Each sensor caught four different brain waves (delta, theta, alpha, and beta) per second. Then, these signals were split in 4-second windows resulting in 512 samples per record and the band energy was extracted for each EEG rhythm. The proposed system was compared with naïve Bayes, Bayes Network, k-nearest neighbors (K-NN), multilayer perceptron (MLP), support vector machine (SVM), J48-C4.5 decision tree, and Bagging classification algorithms. The experimental results showed that the RF algorithm outperformed compared to the other approaches and high levels of accuracy (85.39%) for a 6-class classification are obtained. This method exploits high spatial information acquired from the Emotiv EPOC Flex wearable EEG recording device and examines successfully the potential of this device to be used for BCI wheelchair technology.

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Section: Discussionmentioning

confidence: 99%

EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests

Antoniou

Bozios

Christou

et al. 2021

Sensors

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“…However, if such a BCI is used in game interfaces [71,72] or control interfaces of external devices (wheelchair, manipulator, etc.) [73][74][75], such delay time is critical and negate the advantages of our system in classification accuracy of brain states. One of the possible solutions to this problem could be the use of hybrid fNIRS-EEG BCIs [76][77][78].…”

Section: Discussionmentioning

confidence: 99%

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Hramov

Grubov

Badarin

et al. 2020

Sensors

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Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes. different brain areas (e.g., event-related synchronization/desynchronization) on a sensor level provide important information about the current state of the nervous system and cognitive brain ability [7][8][9][10]. Particular brain states are associated with motor brain activity during either real or imaginary movement.Revealing specific features of spatial brain cortex activity related to real motions and motor imagery of different limbs can be essential not only for basic research in neuroscience, but also for applications in medicine to improve the quality of life of post-traumatic and post-stroke patients using brain-computer interfaces (BCI) for rehabilitation [11][12][13] or to control prostheses and exoskeletons [14]. One of the important BCI functions is online detection of specific features of electromagnetic brain activity using electroencephalography (EEG) [15] or magnetoencephalography (MEG) [16], and transformation of certain patterns into control commands to perform specific actions in the environment without the need of "classical" methods of human-machine interaction [17].Apart from EEG and MEG, other methods are also used to acquire information about brain states. In particular, functional near-infrared spectroscopy (fNIRS) [18,19] is a powerful tool of noninvasive optical imaging successfully used in BCI for registration of brain activity and control command formation [20][21][22]. Control commands for this kind of BCI should not be affected by any muscular activity [23]. Therefore, a study of brain states related to motor imagery is very important for designing such BCI [16,24]. Motor imagery is a mental process by which a person rehearses or simulates a given action with no real motor activity. Some researchers treat motor imagery as a conscious application of unconscious preparation for real motor activity [25]. A number of studies have highlighted common features for real and imaginary motor activity [26][27][28]. One of the common features, important for the BCI development, is that the cortical layout in the primary ...

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“…B RAIN-computer interfaces (BCIs) provide a direct communication pathway between the brain and the external environment by translating the brain activity patterns of a user into commands for an interactive application [1]. BCIs are used in a wide range of areas such as wheelchair operation [2,3], text spelling [4,5], robotic device control [6,7], unmanned aerial vehicle remote control [8], and game play [9]. Among them, the BCI aided spelling application allows the user to select the desired characters and feeds them back on a screen or other output device; this intuitive and easy-to-use approach allows end-users to be more independent and rebuild their social lives to a considerable extent [5,10,11].…”

Section: Introductionmentioning

confidence: 99%

SSVEP-Based Brain-Computer Interface With a Limited Number of Frequencies Based on Dual-Frequency Biased Coding

Jiang

Zhang

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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How to encode as many targets as possible with a limited-frequency resource is a difficult problem in the practical use of a steady-state visual evoked potential (SSVEP) based braincomputer interface (BCI) speller. To solve this problem, this study developed a novel method called dual-frequency biased coding (DFBC) to tag targets in a SSVEP-based 48-character virtual speller, in which each target is encoded with a permutation sequence consisting of two permuted flickering periods that flash at different frequencies. The proposed paradigm was validated by 11 participants in an offline experiment and 7 participants in an online experiment. Three occipital channels (O1, Oz, and O2) were used to obtain the SSVEP signals for identifying the targets. Based on the coding characteristics of the DFBC method, the proposed approach has the ability of self-correction and thus achieves an accuracy of 76.6% and 79.3% for offline and online experiments, respectively, which outperforms the traditional multiple frequencies sequential coding (MFSC) method. This study demonstrates that DFBC is an efficient method for coding a high number of SSVEP targets with a small number of available frequencies.

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A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations

Cited by 113 publications

References 27 publications

EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests

EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

SSVEP-Based Brain-Computer Interface With a Limited Number of Frequencies Based on Dual-Frequency Biased Coding

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