Towards the Recognition of the Emotions of People with Visual Disabilities through Brain–Computer Interfaces

López-Hernández, Jesús Leonardo; González‐Carrasco, Israel; López-Cuadrado, José Luis; Ruíz‐Mezcua, Belén

doi:10.3390/s19112620

“…Start the EEG Data Acquisition in Automatic Mode. 4. According to both visual and auditory indicator, from 2 to 2 seconds, the user should execute one eye-blink.…”

Section: Resultsmentioning

confidence: 99%

“…Brain-Computer Interface is a multidisciplinary research field, which comprises achievements in related scientific and technical areas: artificial intelligence, computer science, mechatronics [1][2][3], signal processing, neuroscience and psychology [4]. The aim of a brain-computer interface system is related to helping people with neuromotor disabilities, who cannot communicate with the outside environment by using natural paths, such as muscles and peripheral nerves.…”

Section: Introductionmentioning

confidence: 99%

Advanced LabVIEW Applications Aimed for the Acquisition, Processing and Classification of the Electroencephalographic Signals Used in a Brain-Computer Interface System

Oa¹

2021

Preprint

2

0

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This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

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“…Brain-Computer Interface is a multidisciplinary research field, which comprises achievements in related scientific and technical areas: artificial intelligence, computer science, mechatronics [1][2][3], signal processing, neuroscience, and psychology [4]. Beyond its various applications in non-clinical fields (digital games, sleep avoidance, mental states monitoring, advertisement, and business), the fundamental aim of a brain-computer interface system is related to helping people with neuromotor disabilities who cannot communicate with the outside environment by using natural paths, such as muscles and peripheral nerves.…”

Section: Introductionmentioning

confidence: 99%

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

Rușanu¹

2022

Preprint

0

View full text Add to dashboard Cite

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

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“…Previously, this paper's authors analyzed the research that proposes the classification of emotions in people with visual disabilities (López-Hernández et al, 2019). The results showed that new approaches that specifically consider people with visual disabilities and the study of their emotions are still required.…”

Section: Introductionmentioning

confidence: 99%

Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals

López-Hernández

¹

,

González‐Carrasco

²

,

López-Cuadrado

³

et al. 2021

Front. Neuroinform.

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Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is the brain–computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.

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Towards the Recognition of the Emotions of People with Visual Disabilities through Brain–Computer Interfaces

Cited by 8 publications

References 64 publications

Advanced LabVIEW Applications Aimed for the Acquisition, Processing and Classification of the Electroencephalographic Signals Used in a Brain-Computer Interface System

Advanced LabVIEW Applications Aimed for the Acquisition, Processing and Classification of the Electroencephalographic Signals Used in a Brain-Computer Interface System

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals

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