A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface

Salazar-Ramirez, Asier; Martín, Jesús Izquierdo; Martínez, Raquel; Arruti, Andoni; Muguerza, Javier; Sierra, Basilio

doi:10.1371/journal.pone.0218181

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…A basic linear model that did not take into account these sets, it was defective from the onset. However, a hierarchical model allowed us to take into account the effects of these sets, as well as the relations between them [36,37,38]. In the present study, we used the brain connectivity method (i.e., PLV) to hierarchically classify the neural activities of the left and right hand response inhibitions.…”

Section: Resultsmentioning

confidence: 99%

Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model

Chikara

2019

Sensors

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Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research.

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Section: Resultsmentioning

confidence: 99%

Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model

Chikara

2019

Sensors

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“…This makes it an effective instrument for investigating brain function and spotting irregularities coupled with depression. Moreover, EEG has a very high temporal resolution, meaning it can portray variations in brain activity in real-time with millisecond precision [23][24][25]. This is fundamental for sensing rapid fluctuations and dynamics of brain activity connected to emotional processing, which can be influential in understanding depression.…”

Section: Introductionmentioning

confidence: 99%

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

Khan,

Umar Saeed,

Frnda

et al. 2024

PLoS ONE

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Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively. This study presents an EEG-based mental depressive disorder detection mechanism using a publicly available EEG dataset called Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). This study uses EEG data acquired from 55 participants using 3 electrodes in the resting-state condition. Twelve temporal domain features are extracted from the EEG data by creating a non-overlapping window of 10 seconds, which is presented to a novel feature selection mechanism. The feature selection algorithm selects the optimum chunk of attributes with the highest discriminative power to classify the mental depressive disorders patients and healthy controls. The selected EEG attributes are classified using three different classification algorithms i.e., Best- First (BF) Tree, k-nearest neighbor (KNN), and AdaBoost. The highest classification accuracy of 96.36% is achieved using BF-Tree using a feature vector length of 12. The proposed mental depressive classification scheme outperforms the existing state-of-the-art depression classification schemes in terms of the number of electrodes used for EEG recording, feature vector length, and the achieved classification accuracy. The proposed framework could be used in psychiatric settings, providing valuable support to psychiatrists.

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A hybrid ensemble voting-based residual attention network for motor imagery EEG Classification

Jindal,

Upadhyay,

Singh

2024

Analog Integr Circ Sig Process

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Multi-class motor imagery Electroencephalography (EEG) activity decoding has always been challenging for the development of Brain Computer Interface (BCI) system. Deep learning has recently emerged as a powerful approach for BCI system development using EEG activity. However, the EEG activity analysis and classification should be robust, automated and accurate. Currently, available BCI systems perform well for binary task identification whereas, multi-class classification of EEG activity for BCI applications is still a challenging task. In this work, a hybrid Residual Attention ensemble voting classifier model is developed for EEG-based Motor Imagery-Brain Computer Interface (MI-BCI) task classification. The Time-Frequency Representation (TFR) of the multi-class EEG activity is generated using Transient Extracting Transform (TET). The TFR spectrogram images are input to the designed Residual Attention ensemble voting classifier model for training and classification purposes. The model is evaluated using different fusion strategies viz. feature-level and score-level fusion of layers. The proposed model is evaluated on two MI-BCI datasets, BCI competition IV 2a and BCI competition III 3a, yielding the highest classification accuracies of 88.14 % and 93.13 %, respectively. The results obtained on a large multi-class MI-BCI dataset confirm that the proposed hybrid Residual Attention ensemble voting classifier model significantly outperforms the conventional algorithm and achieves significantly high classification accuracy for the feature-level fusion of layers. The developed framework aids in identifying different tasks for multi-class MI-BCI EEG activity.

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A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface

Cited by 4 publications

References 34 publications

Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model

Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model

A machine learning based depression screening framework using temporal domain features of the electroencephalography signals

A hybrid ensemble voting-based residual attention network for motor imagery EEG Classification

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