Research

14 pages, 649 KiB

Open AccessArticle

A More Fine-Grained Aspect–Sentiment–Opinion Triplet Extraction Task

by Yuncong Li, Fang Wang and Sheng-hua Zhong

Mathematics 2023, 11(14), 3165; https://0-doi-org.brum.beds.ac.uk/10.3390/math11143165 - 19 Jul 2023

Cited by 1 | Viewed by 1189

Sentiment analysis aims to systematically study affective states and subjective information in digital text through computational methods. Aspect Sentiment Triplet Extraction (ASTE), a subtask of sentiment analysis, aims to extract aspect term, sentiment and opinion term triplets from sentences. However, some ASTE’s extracted [...] Read more.

Sentiment analysis aims to systematically study affective states and subjective information in digital text through computational methods. Aspect Sentiment Triplet Extraction (ASTE), a subtask of sentiment analysis, aims to extract aspect term, sentiment and opinion term triplets from sentences. However, some ASTE’s extracted triplets are not self-contained, as they reflect the sentence’s sentiment toward the aspect term, not the sentiment between the aspect and opinion terms. These triplets are not only unhelpful to people, but can also be detrimental to downstream tasks. In this paper, we introduce a more nuanced task, Aspect–Sentiment–Opinion Triplet Extraction (ASOTE), which also extracts aspect term, sentiment and opinion term triplets. However, the sentiment in a triplet extracted with ASOTE is the sentiment of the aspect term and opinion term pair. We build four datasets for ASOTE. A Position-aware BERT-based Framework (PBF) is proposed to address ASOTE. PBF first extracts aspect terms from sentences. For each extracted aspect term, PBF generates an aspect term-specific sentence representation, considering the aspect term’s position. It then extracts associated opinion terms and predicts the sentiments of the aspect–opinion term pairs based on the representation. In the experiments on the four datasets, PBF has set a benchmark performance on the novel ASOTE task. Full article

(This article belongs to the Special Issue From Brain Science to Artificial Intelligence)

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18 pages, 2805 KiB

Open AccessArticle

EEG-Based Emotion Recognition via Knowledge-Integrated Interpretable Method

by Ying Zhang, Chen Cui and Shenghua Zhong

Mathematics 2023, 11(6), 1424; https://0-doi-org.brum.beds.ac.uk/10.3390/math11061424 - 15 Mar 2023

Cited by 2 | Viewed by 1306

Abstract

Despite achieving success in many domains, deep learning models remain mostly black boxes, especially in electroencephalogram (EEG)-related tasks. Meanwhile, understanding the reasons behind model predictions is quite crucial in assessing trust and performance promotion in EEG-related tasks. In this work, we explore the [...] Read more.

Despite achieving success in many domains, deep learning models remain mostly black boxes, especially in electroencephalogram (EEG)-related tasks. Meanwhile, understanding the reasons behind model predictions is quite crucial in assessing trust and performance promotion in EEG-related tasks. In this work, we explore the use of representative interpretable models to analyze the learning behavior of convolutional neural networks (CNN) in EEG-based emotion recognition. According to the interpretable analysis, we find that similar features captured by our model and state-of-the-art model are consistent with previous brain science findings. Next, we propose a new model by integrating brain science knowledge with the interpretability analysis results in the learning process. Our knowledge-integrated model achieves better recognition accuracy on standard EEG-based recognition datasets. Full article

(This article belongs to the Special Issue From Brain Science to Artificial Intelligence)

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16 pages, 304 KiB

Open AccessArticle

Convolutional Neural Network for Closed-Set Identification from Resting State Electroencephalography

by Chi Qin Lai, Haidi Ibrahim, Shahrel Azmin Suandi and Mohd Zaid Abdullah

Mathematics 2022, 10(19), 3442; https://0-doi-org.brum.beds.ac.uk/10.3390/math10193442 - 22 Sep 2022

Cited by 6 | Viewed by 1436

Abstract

In line with current developments, biometrics is becoming an important technology that enables safer identification of individuals and more secure access to sensitive information and assets. Researchers have recently started exploring electroencephalography (EEG) as a biometric modality thanks to the uniqueness of EEG [...] Read more.

In line with current developments, biometrics is becoming an important technology that enables safer identification of individuals and more secure access to sensitive information and assets. Researchers have recently started exploring electroencephalography (EEG) as a biometric modality thanks to the uniqueness of EEG signals. A new architecture for a convolutional neural network (CNN) that uses EEG signals is suggested in this paper for biometric identification. A CNN does not need complex signal pre-processing, feature extraction, and feature selection stages. The EEG datasets utilized in this research are the resting state eyes open (REO) and the resting state eyes closed (REC) EEG. Extensive experiments were performed to design this deep CNN architecture. These experiments showed that a CNN architecture with eleven layers (eight convolutional layers, one average pooling layer, and two fully connected layers) with an Adam optimizer resulted in the highest accuracy. The CNN architecture proposed here was compared to existing models for biometrics using the same dataset. The results show that the proposed method outperforms the other task-free paradigm CNN biometric identification models, with an identification accuracy of 98.54%. Full article

(This article belongs to the Special Issue From Brain Science to Artificial Intelligence)

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17 pages, 4314 KiB

Open AccessArticle

Using Neural Networks to Uncover the Relationship between Highly Variable Behavior and EEG during a Working Memory Task with Distractors

by Christine Beauchene, Silu Men, Thomas Hinault, Susan M. Courtney and Sridevi V. Sarma

Mathematics 2022, 10(11), 1848; https://0-doi-org.brum.beds.ac.uk/10.3390/math10111848 - 27 May 2022

Viewed by 1615

Abstract

Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and [...] Read more.

Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants (

r = 0.53 \pm 0.08

). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants (

r = 0.87 \pm 0.04

). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions. Full article

(This article belongs to the Special Issue From Brain Science to Artificial Intelligence)

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18 pages, 401 KiB

Open AccessArticle

GATSMOTE: Improving Imbalanced Node Classification on Graphs via Attention and Homophily

by Yongxu Liu, Zhi Zhang, Yan Liu and Yao Zhu

Mathematics 2022, 10(11), 1799; https://0-doi-org.brum.beds.ac.uk/10.3390/math10111799 - 24 May 2022

Cited by 7 | Viewed by 1757

Abstract

In recent decades, non-invasive neuroimaging techniques and graph theories have enabled a better understanding of the structural patterns of the human brain at a macroscopic level. As one of the most widely used non-invasive techniques, an electroencephalogram (EEG) may collect non-neuronal signals from [...] Read more.

In recent decades, non-invasive neuroimaging techniques and graph theories have enabled a better understanding of the structural patterns of the human brain at a macroscopic level. As one of the most widely used non-invasive techniques, an electroencephalogram (EEG) may collect non-neuronal signals from “bad channels”. Automatically detecting these bad channels represents an imbalanced classification task; research on the topic is rather limited. Because the human brain can be naturally modeled as a complex graph network based on its structural and functional characteristics, we seek to extend previous imbalanced node classification techniques to the bad-channel detection task. We specifically propose a novel edge generator considering the prominent small-world organization of the human brain network. We leverage the attention mechanism to adaptively calculate the weighted edge connections between each node and its neighboring nodes. Moreover, we follow the homophily assumption in graph theory to add edges between similar nodes. Adding new edges between nodes sharing identical labels shortens the path length, thus facilitating low-cost information messaging. Full article

(This article belongs to the Special Issue From Brain Science to Artificial Intelligence)

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16 pages, 3082 KiB

Open AccessArticle

The Effect of Alpha Neurofeedback Training on Cognitive Performance in Healthy Adults

by Rab Nawaz, Humaira Nisar, Vooi Voon Yap and Chi-Yi Tsai

Mathematics 2022, 10(7), 1095; https://0-doi-org.brum.beds.ac.uk/10.3390/math10071095 - 29 Mar 2022

Cited by 8 | Viewed by 3530

Abstract

This study investigates the effect of long-term alpha neurofeedback training (NFT) in healthy adults using music stimuli. The optimal protocol for future research is presented in this study. The data from 40 healthy participants, divided into two groups (NFT group and Control group), [...] Read more.

This study investigates the effect of long-term alpha neurofeedback training (NFT) in healthy adults using music stimuli. The optimal protocol for future research is presented in this study. The data from 40 healthy participants, divided into two groups (NFT group and Control group), were analyzed in the current study. We found a significantly enhanced alpha rhythm after training in the NFT group which was not observed in the control group. The immediate subsequent effects were greater in more than 80% of the sessions from the initial recordings. Stroop task and behavioral questionnaires, mini-mental state exam (MMSE), and perceived stress scale (PSS) did not reveal any training-specific changes. Within-training session effects were significant from the baseline and were more pronounced at the beginning of the session as compared to the end of the session. It is also observed that a shorter session length with multiple sessions may be more effective than a long and continuous run of a single session. Full article

(This article belongs to the Special Issue From Brain Science to Artificial Intelligence)

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20 pages, 29573 KiB

Open AccessArticle

Continuous Hybrid BCI Control for Robotic Arm Using Noninvasive Electroencephalogram, Computer Vision, and Eye Tracking

by Baoguo Xu, Wenlong Li, Deping Liu, Kun Zhang, Minmin Miao, Guozheng Xu and Aiguo Song

Mathematics 2022, 10(4), 618; https://0-doi-org.brum.beds.ac.uk/10.3390/math10040618 - 17 Feb 2022

Cited by 26 | Viewed by 3640

Abstract

The controlling of robotic arms based on brain–computer interface (BCI) can revolutionize the quality of life and living conditions for individuals with physical disabilities. Invasive electroencephalography (EEG)-based BCI has been able to control multiple degrees of freedom (DOFs) robotic arms in three dimensions. [...] Read more.

The controlling of robotic arms based on brain–computer interface (BCI) can revolutionize the quality of life and living conditions for individuals with physical disabilities. Invasive electroencephalography (EEG)-based BCI has been able to control multiple degrees of freedom (DOFs) robotic arms in three dimensions. However, it is still hard to control a multi-DOF robotic arm to reach and grasp the desired target accurately in complex three-dimensional (3D) space by a noninvasive system mainly due to the limitation of EEG decoding performance. In this study, we propose a noninvasive EEG-based BCI for a robotic arm control system that enables users to complete multitarget reach and grasp tasks and avoid obstacles by hybrid control. The results obtained from seven subjects demonstrated that motor imagery (MI) training could modulate brain rhythms, and six of them completed the online tasks using the hybrid-control-based robotic arm system. The proposed system shows effective performance due to the combination of MI-based EEG, computer vision, gaze detection, and partially autonomous guidance, which drastically improve the accuracy of online tasks and reduce the brain burden caused by long-term mental activities. Full article

(This article belongs to the Special Issue From Brain Science to Artificial Intelligence)

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11 pages, 3691 KiB

Open AccessArticle

A Novel DE-CNN-BiLSTM Multi-Fusion Model for EEG Emotion Recognition

by Fachang Cui, Ruqing Wang, Weiwei Ding, Yao Chen and Liya Huang

Mathematics 2022, 10(4), 582; https://0-doi-org.brum.beds.ac.uk/10.3390/math10040582 - 13 Feb 2022

Cited by 27 | Viewed by 3275

Abstract

As a long-standing research topic in the field of brain–computer interface, emotion recognition still suffers from low recognition accuracy. In this research, we present a novel model named DE-CNN-BiLSTM deeply integrating the complexity of EEG signals, the spatial structure of brain and temporal [...] Read more.

As a long-standing research topic in the field of brain–computer interface, emotion recognition still suffers from low recognition accuracy. In this research, we present a novel model named DE-CNN-BiLSTM deeply integrating the complexity of EEG signals, the spatial structure of brain and temporal contexts of emotion formation. Firstly, we extract the complexity properties of the EEG signal by calculating Differential Entropy in different time slices of different frequency bands to obtain 4D feature tensors according to brain location. Subsequently, the 4D tensors are input into the Convolutional Neural Network to learn brain structure and output time sequences; after that Bidirectional Long-Short Term Memory is used to learn past and future information of the time sequences. Compared with the existing emotion recognition models, the new model can decode the EEG signal deeply and extract key emotional features to improve accuracy. The simulation results show the algorithm achieves an average accuracy of 94% for DEAP dataset and 94.82% for SEED dataset, confirming its high accuracy and strong robustness. Full article

(This article belongs to the Special Issue From Brain Science to Artificial Intelligence)

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19 pages, 35302 KiB

Open AccessArticle

The Effect of Music Listening on EEG Functional Connectivity of Brain: A Short-Duration and Long-Duration Study

by Danyal Mahmood, Humaira Nisar, Vooi Voon Yap and Chi-Yi Tsai

Mathematics 2022, 10(3), 349; https://0-doi-org.brum.beds.ac.uk/10.3390/math10030349 - 24 Jan 2022

Cited by 14 | Viewed by 10756

Abstract

Music is considered a powerful brain stimulus, as listening to it can activate several brain networks. Music of different kinds and genres may have a different effect on the human brain. The goal of this study is to investigate the change in the [...] Read more.

Music is considered a powerful brain stimulus, as listening to it can activate several brain networks. Music of different kinds and genres may have a different effect on the human brain. The goal of this study is to investigate the change in the brain’s functional connectivity (FC) when music is used as a stimulus. Secondly, the effect of listening to the subject’s favorite music is compared with listening to specifically formulated relaxing music with alpha binaural beats. Finally, the effect of the duration of music listening is studied. Subjects’ electroencephalographic (EEG) signals were captured as they listened to favorite and relaxing music. After preprocessing and artifact removal, the EEG recordings were decomposed into the delta, theta, alpha, and beta frequency bands, and the grand-averaged connectivity matrices were generated using Inter-Site Phase Clustering (ISPC) for each frequency band and each type of music. Furthermore, each lobe of the brain was analyzed separately to understand the effect of music on specific regions of the brain. EEG-FC among different channels was accessed by using graph theory and Network-based Statistics (NBS). To determine the significance of the changes in brain networks after listening to music, statistical analysis was conducted using Analysis of Variance (ANOVA) and t-test. The study of listening to music for a short duration verifies that either favorite or preferred music can affect the FC of the subject and induce a relaxation state. The short duration study also verifies a significant (ANOVA and t-test: p < 0.05) effectiveness of relaxing music over favorite music to induce relaxation and alertness in the subject. In the study of long duration, it is concluded that listening to relaxing music can increase functional connectivity and connections strength in the frontal lobe of the subject. A significant increase (ANOVA and t-test: p < 0.05) in FC in alpha and theta band and a significant decrease (ANOVA and t-test: p < 0.05) in FC in beta band in the frontal and parietal lobe of the brain verifies the hypothesis that the relaxing music can help the subject to achieve relaxation, activeness, and alertness. Full article

(This article belongs to the Special Issue From Brain Science to Artificial Intelligence)

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