RETRACTED ARTICLE: A new EEG software that supports emotion recognition by using an autonomous approach

Castro-Muñoz, Roberto; Olivares, Rodrigo; Taramasco, Carla; Villarroel, Rodolfo; Soto, Ricardo; Alonso-Sánchez, María Francisca; Merino, Erick; Albuquerque, Victor Hugo C. de

doi:10.1007/s00521-018-3925-z

Cited by 13 publications

(10 citation statements)

References 37 publications

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“…The other findings are related to typical research on emotion recognition, but the numbers of experimental subjects were very small (some have several subjects) [55]- [58]. Moreover, EEG software for automated emotion recognition to support consumer grade devices, including OpenBCI, was proposed in late 2018 [59]. Thus, it could be inferred that the demand for OpenBCI is increasing.…”

Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 82%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. Additional 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition.

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Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 82%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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“…The window size of 6 S was found to give the best AVR. We also compared our results to the other state-of-the-art results obtained using MAHNOB database, as shown in Table 10 [68][69][70]. The results indicate that the proposed model outperforms the state-of-the-art concerning EEG emotion recognition.…”

Section: Third Experiment: Eeg Emotion Recognitionmentioning

confidence: 82%

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

2019

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In multi-modal emotion aware frameworks, it is essential to estimate the emotional features then fuse them to different degrees. This basically follows either a feature-level or decision-level strategy. In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. To overcome issues of feature extraction and multi-modal fusion, hybrid fuzzy-evolutionary computation methodologies are employed to demonstrate ultra-strong capability of learning features and dimensionality reduction. This paper proposes a novel multi-modal emotion aware system by fusing speech with EEG modalities. Firstly, a mixing feature set of speaker-dependent and independent characteristics is estimated from speech signal. Further, EEG is utilized as inner channel complementing speech for more authoritative recognition, by extracting multiple features belonging to time, frequency, and time–frequency. For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. To fuse speech with EEG information, a separate classifier is used for each modality, then output is computed by integrating their posterior probabilities. Results show the superiority of the proposed model, where the overall performance in terms of accuracy average rates is 98.06%, and 97.28%, and 98.53% for EEG, speech, and multi-modal recognition, respectively. The proposed model is also applied to two public databases for speech and EEG, namely: SAVEE and MAHNOB, which achieve accuracies of 98.21% and 98.26%, respectively.

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“…Although there are many different optimization methods in the literature, particle swarm optimization (PSO) algorithm, which is one of the herd-based optimization algorithms that are among the heuristic methods, is used. Although the starting point of the standard PSO method is to detect continuous variables, since electrode selection is a binary variable, it can be used as binary PSO (Binary PSO -BPSO) with sigmoid transformation [6,7,24,25,26,27]. In the study, it is aimed to determine the most effective electrodes to be used on a person basis, to decrease the processing load by reducing the data size obtained and to increase the classification performance.…”

Section: Open Accessmentioning

confidence: 99%

Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems

Arıcan¹,

Polat²

2020

AIS

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Social participation of people with disabilities is tried to be increased with state-supported projects recently. However, even in neuromuscular diseases such as Motor Neurone Disease (MND), Full Sliding Status (TSD), even the communication skills of individuals are interrupted. Brain-Computer Interfaces (BBA), which have a few decades of history and an increasing number of studies with exponential momentum, are being developed to enable individuals with such disorders to communicate with their environment. Spelling systems are BBA systems that detect the letters that the person focuses on the matrix of letters and numbers on a screen and convert them into text through the application. In this context, with the random flashing of the letters on the screen, it aims to detect the electrical changes occurring in the brain as a result of the stimulus given to the person. Research reveals that the stimulus that the individual encounters cause an amplitude in the EEG signal called P300, between 250 and 500 ms. Brain-computer interfaces are used through EEG signals to provide environmental interactions for individuals with restricted movements due to stroke or neurodegenerative diseases. The multi-channel structure of EEG signals both increases system cost and reduces processing speed. For this reason, reducing the system cost by detecting more active electrodes during the process increases the accessibility of people. In this context, the use of optimization techniques in electrode selection is used to determine the most effective channels by a random selection method. In the study, particle herd optimization algorithm, one of the herd-based optimization techniques, was used with two classifiers, SVM and Boosted Tree, and the eight most frequently selected channels were determined to improve system performance in terms of speed and accuracy.

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RETRACTED ARTICLE: A new EEG software that supports emotion recognition by using an autonomous approach

Cited by 13 publications

References 37 publications

Consumer Grade Brain Sensing for Emotion Recognition

Consumer Grade Brain Sensing for Emotion Recognition

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems

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