EmoPercept: EEG-based emotion classification through perceiver

Aadam, None; Tubaishat, Abdallah; Al-Obeidat, Feras; Halim, Zahid; Waqas, Muhammad; Qayum, Fawad

doi:10.1007/s00500-021-06578-4

Cited by 12 publications

(1 citation statement)

References 33 publications

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“…Wang et al [44] used a cascade Transformer combined with prior knowledge to determine spatial dependence between different brain regions. Abdallah Tubaishat et al [45] used a novel architecture based on Transformer to process multimodal neural signals and identify emotions. Arjun et al [46] divided EEG signals or time-frequency images generated by wavelet transform into patches by using a Vision Transformer, and subsequently using a Transformer encoder for emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Transformer-based ensemble deep learning model for EEG-based emotion recognition

Si¹,

Huang²,

Sun³

et al. 2023

Brain Science Advances

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Emotion recognition is one of the most important research directions in the field of brain-computer interface (BCI). However, to conduct electroencephalogram (EEG)-based emotion recognition, there exist difficulties regarding EEG signal processing; moreover, the performance of classification models in this regard is restricted. To counter these issues, the 2022 World Robot Contest successfully held an affective BCI competition, thus promoting the innovation of EEG-based emotion recognition. In this paper, we propose the Transformer-based ensemble (TBEM) deep learning model. TBEM comprises two models: a pure convolutional neural network (CNN) model and a cascaded CNN-Transformer hybrid model. The proposed model won the abovementioned affective BCI competition's final championship in the 2022 World Robot Contest, demonstrating the effectiveness of the proposed TBEM deep learning model for EEG-based emotion recognition.

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Section: Introductionmentioning

confidence: 99%

Transformer-based ensemble deep learning model for EEG-based emotion recognition

Si¹,

Huang²,

Sun³

et al. 2023

Brain Science Advances

View full text Add to dashboard Cite

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Assessment of Parkinson’s Disease Severity Using Gait Data: A Deep Learning-Based Multimodal Approach

Faiem,

Asuroglu,

Acici

et al. 2024

Communications in Computer and Information Science

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The ability to regularly assess Parkinson’s disease (PD) symptoms outside of complex laboratories supports remote monitoring and better treatment management. Multimodal sensors are beneficial for sensing different motor and non-motor symptoms, but simultaneous analysis is difficult due to complex dependencies between different modalities and their different format and data properties. Multimodal machine learning models can analyze such diverse modalities together, thereby enhancing holistic understanding of the data and overall patient state. The Unified Parkinson’s Disease Rating Scale (UPDRS) is commonly used for PD symptoms severity assessment. This study proposes a Perceiver-based multimodal machine learning framework to predict UPDRS scores.We selected a gait dataset of 93 PD patients and 73 control subjects from the PhysioNet repository. This dataset includes two-minute walks from each participant using 16 Ground Reaction Force (GRF) sensors, placing eight on each foot. This experiment used both raw gait timeseries signals and extracted features from these GRF sensors. The Perceiver architecture’s hyperparameters were selected manually and through Genetic Algorithms (GA). The performance of the framework was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and linear Correlation Coefficient (CC).Our multimodal approach achieved a MAE of 2.23 ± 1.31, a RMSE of 5.75 ± 4.16 and CC of 0.93 ± 0.08 in predicting UPDRS scores, outperforming previous studies in terms of MAE and CC.This multimodal framework effectively integrates different data modalities, in this case illustrating by predicting UPDRS scores using sensor data. It can be applied to diverse decision support applications of similar natures where multimodal analysis is needed.

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Editorial on decision support system for development of intelligent applications

et al. 2022

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EmoPercept: EEG-based emotion classification through perceiver

Cited by 12 publications

References 33 publications

Transformer-based ensemble deep learning model for EEG-based emotion recognition

Transformer-based ensemble deep learning model for EEG-based emotion recognition

Assessment of Parkinson’s Disease Severity Using Gait Data: A Deep Learning-Based Multimodal Approach

Editorial on decision support system for development of intelligent applications

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