Motor imagery classification using feature relevance analysis: An Emotiv-based BCI system

Hurtado-Rincón, Juana Valeria; Rojas-Jaramillo, Sebastian; Ricardo-Cespedes, Y.; Álvarez-Meza, Andrés Marino; Castellanos-Domínguez, G.

doi:10.1109/stsiva.2014.7010165

Cited by 13 publications

(2 citation statements)

References 15 publications

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“…Furthermore, it is important to state that the signals in dataset B had constraints in AF3 and AF4 electrodes, which may be important electrodes according to Lin and Lo [60] and Muñoz et al [61], thus decreasing the obtained accuracy and the F1-score. The average accuracy of other authors using the EPOC and the magnitude of frequency components or the power spectral density (square of the magnitude) as features was 74-100% for Abiyev et al [57], 70% for Hurtado-Rincon et al [59], and 86-92% for Lin and Lo [60], and Siribunyaphat and Punsawad [67]. More recent works [49,68,69] have also achieved important F1-scores, using different EEG headsets.…”

Section: Discussionmentioning

confidence: 93%

Applications of Brain Wave Classification for Controlling an Intelligent Wheelchair

Avelar,

Almeida,

Faria

et al. 2024

Technologies

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The independence and autonomy of both elderly and disabled people have been a growing concern in today’s society. Therefore, wheelchairs have proven to be fundamental for the movement of these people with physical disabilities in the lower limbs, paralysis, or other type of restrictive diseases. Various adapted sensors can be employed in order to facilitate the wheelchair’s driving experience. This work develops the proof concept of a brain–computer interface (BCI), whose ultimate final goal will be to control an intelligent wheelchair. An event-related (de)synchronization neuro-mechanism will be used, since it corresponds to a synchronization, or desynchronization, in the mu and beta brain rhythms, during the execution, preparation, or imagination of motor actions. Two datasets were used for algorithm development: one from the IV competition of BCIs (A), acquired through twenty-two Ag/AgCl electrodes and encompassing motor imagery of the right and left hands, and feet; and the other (B) was obtained in the laboratory using an Emotiv EPOC headset, also with the same motor imaginary. Regarding feature extraction, several approaches were tested: namely, two versions of the signal’s power spectral density, followed by a filter bank version; the use of respective frequency coefficients; and, finally, two versions of the known method filter bank common spatial pattern (FBCSP). Concerning the results from the second version of FBCSP, dataset A presented an F1-score of 0.797 and a rather low false positive rate of 0.150. Moreover, the correspondent average kappa score reached the value of 0.693, which is in the same order of magnitude as 0.57, obtained by the competition. Regarding dataset B, the average value of the F1-score was 0.651, followed by a kappa score of 0.447, and a false positive rate of 0.471. However, it should be noted that some subjects from this dataset presented F1-scores of 0.747 and 0.911, suggesting that the movement imagery (MI) aptness of different users may influence their performance. In conclusion, it is possible to obtain promising results, using an architecture for a real-time application.

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Section: Discussionmentioning

confidence: 93%

Applications of Brain Wave Classification for Controlling an Intelligent Wheelchair

Avelar,

Almeida,

Faria

et al. 2024

Technologies

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“…Emotiv is a low-cost, portable EEG acquisition system and thus, in recent years, researchers have used it in detection of P300, steady state visually evoked potential, and ERD/ERS waveform. Hurtado-Rincon et al [52] and Dharmasena et al [53] has successfully classified between [54] have acquired brain signals related to movement to successfully drive a powered orthosis tasked at opening and closing of the patient's hand. Fig.…”

Section: A Detection Of Erd/ers Patternsmentioning

confidence: 99%

A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control

Bhattacharyya

Shimoda²,

Hayashibe

2016

IEEE Trans. Syst. Man Cybern, Syst.

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This paper proposes a novel brain-machine interfacing (BMI) paradigm for control of a multijoint redundant robot system. Here, the user would determine the direction of end-point movement of a 3-degrees of freedom (DOF) robot arm using motor imagery electroencephalography signal with coadaptive decoder (adaptivity between the user and the decoder) while a synergetic motor learning algorithm manages a peripheral redundancy in multi-DOF joints toward energy optimality through tacit learning. As in human motor control, torque control paradigm is employed for a robot to be adaptive to the given physical environment. The dynamic condition of the robot arm is taken into consideration by the learning algorithm. Thus, the user needs to only think about the end-point movement of the robot arm, which allows simultaneous multijoints control by BMI. The support vector machine-based decoder designed in this paper is adaptive to the changing mental state of the user. Online experiments reveals that the users successfully reach their targets with an average decoder accuracy of over 75% in different end-point load conditions. Index Terms-Brain-machine interfacing (BMI), co-adaptive decoder, joint redundancy, multijoint robot, synergetic learning control, tacit learning. I. INTRODUCTION A S OF today, brain-machine interfacing (BMI) [or braincomputer interfacing (BCI)] is one of the fastest growing areas of research that provides a unique course of communication between a human and a machine (or device) without any neuro-muscular intervention [1]. BMI was initially conceived to provide rehabilitative and assistive solutions [2], [3] to patients suffering from neuromuscular degenerative diseases, such as amyotropic lateral sclerosis, cervical spinal injury, paralysis, or amputee [4].

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Decoding of Imaginary Motor Movements of Fists Applying Spatial Filtering in a BCI Simulated Application

Boelts

Cerquera

Ruiz-Olaya

2015

Artificial Computation in Biology and Medicine

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Motor imagery classification using feature relevance analysis: An Emotiv-based BCI system

Cited by 13 publications

References 15 publications

Applications of Brain Wave Classification for Controlling an Intelligent Wheelchair

Applications of Brain Wave Classification for Controlling an Intelligent Wheelchair

A Synergetic Brain-Machine Interfacing Paradigm for Multi-DOF Robot Control

Decoding of Imaginary Motor Movements of Fists Applying Spatial Filtering in a BCI Simulated Application

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