scholarly journals Emotion Recognition Using Electroencephalography Signals of Older People for Reminiscence Therapy

2022 ◽  
Vol 12 ◽  
Author(s):  
Lei Jiang ◽  
Panote Siriaraya ◽  
Dongeun Choi ◽  
Noriaki Kuwahara
Keyword(s):  
Young People ◽  
Older People ◽  
Emotion Recognition ◽  
Support Systems ◽  
Negative Emotions ◽  
Eeg Signals ◽  
Communication Support ◽  
Reminiscence Therapy ◽  
Eeg Data ◽  
Positive And Negative Emotions

Objective: Numerous communication support systems based on reminiscence therapy have been developed. However, when using communication support systems, the emotional assessment of older people is generally conducted using verbal feedback or questionnaires. The purpose of this study is to investigate the feasibility of using Electroencephalography (EEG) signals for automatic emotion recognition during RT for older people.Participants: Eleven older people (mean 71.25, SD 4.66) and seven young people (mean 22.4, SD 1.51) participated in the experiment.Methods: Old public photographs were used as material for reminiscence therapy. The EEG signals of the older people were collected while the older people and young people were talking about the contents of the photos. Since emotions change slowly and responses are characterized by delayed effects in EEG, the depth models LSTM and Bi-LSTM were selected to extract complex emotional features from EEG signals for automatic recognition of emotions.Results: The EEG data of 8 channels were inputted into the LSTM and Bi-LSTM models to classify positive and negative emotions. The recognition highest accuracy rate of the two models were 90.8% and 95.8% respectively. The four-channel EEG data based Bi-LSTM also reached 94.4%.Conclusion: Since the Bi-LSTM model could tap into the influence of “past” and “future” emotional states on the current emotional state in the EEG signal, we found that it can help improve the ability to recognize positive and negative emotions in older people. In particular, it is feasible to use EEG signals without the necessity of multimodal physiological signals for emotion recognition in the communication support systems for reminiscence therapy when using this model.

scholarly journals A Library of Old Photos Supporting Conversation of Two Generations Serving Reminiscence Therapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Lei Jiang ◽  
Panote Siriaraya ◽  
Dongeun Choi ◽  
Noriaki Kuwahara
Keyword(s):  
Young People ◽  
Older People ◽  
Institutional Care ◽  
Strong Impact ◽  
Reminiscence Therapy ◽  
Family Patterns ◽  
Two Generations ◽  
Aged Society ◽  
Assessment Scales ◽  
Emotion Assessment

In Japan, a shift in family patterns has led to a sense of social isolation among older people, which increases the risk of major neurocognitive disorder. Interventions for them using old photos to implement reminiscence therapy (RT) have been proved to be effective. A super-aged society has in turn led to a shortage of medical resources and older people prefer home care over institutional care. Therefore, there is an urgent need for volunteers to help in RT. However, the age of volunteers tends to be increasingly younger. The lack of knowledge and experience of the past for the young volunteers makes it difficult for them to select appropriate stimulated materials. To improve this situation, a library of old photos for RT was developed to support conversation between the two generations. A two-factor experiment and emotion assessment scales were designed to explore the effect of different old photo types on the fluency of conversation between the two generations and their emotion. It was found that the types of old photos have little effect on older people and that conversations were almost pleasant. However, the pleasantness of older people was enhanced when using photos that they wanted to talk about (P = 0.006). Meanwhile, pleasure in conversation of the older people increased with the attention of the young people to the topic (R = 0.304, p < 0.001). Conversely, photo type has a strong impact on young people. When photos are selected that older people do not want to talk about or photos that young people do not know the content and are not interested in, concern for the topic of young people drops dramatically. Therefore, when RT, it is important to avoid using the types of photos above that cause a drop in younger people's attention.

scholarly journals Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 683 ◽  
Author(s):  
Jiahui Cai ◽  
Wei Chen ◽  
Zhong Yin
Keyword(s):  
Feature Selection ◽  
Emotion Recognition ◽  
Least Square ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Affective States ◽  
Eeg Signals ◽  
Subject Selection ◽  
Automation Systems ◽  
Eeg Data

Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.

scholarly journals EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5092
Author(s):  
Tran-Dac-Thinh Phan ◽  
Soo-Hyung Kim ◽  
Hyung-Jeong Yang ◽  
Guee-Sang Lee
Keyword(s):  
Emotion Recognition ◽  
Binary Classification ◽  
Eeg Signals ◽  
Frequency Bands ◽  
Multi Scale ◽  
Eeg Data ◽  
Crucial Information ◽  
The Time Domain ◽  
Electroencephalogram Eeg ◽  
Selection Of

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognition based on EEG signals heavily relies on the features and their delineation, which requires the selection of feature categories converted from the raw signals and types of expressions that could display the intrinsic properties of an individual signal or a group of them. Moreover, the correlation or interaction among channels and frequency bands also contain crucial information for emotional state prediction, and it is commonly disregarded in conventional approaches. Therefore, in our method, the correlation between 32 channels and frequency bands were put into use to enhance the emotion prediction performance. The extracted features chosen from the time domain were arranged into feature-homogeneous matrices, with their positions following the corresponding electrodes placed on the scalp. Based on this 3D representation of EEG signals, the model must have the ability to learn the local and global patterns that describe the short and long-range relations of EEG channels, along with the embedded features. To deal with this problem, we proposed the 2D CNN with different kernel-size of convolutional layers assembled into a convolution block, combining features that were distributed in small and large regions. Ten-fold cross validation was conducted on the DEAP dataset to prove the effectiveness of our approach. We achieved the average accuracies of 98.27% and 98.36% for arousal and valence binary classification, respectively.

scholarly journals The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

2022 ◽  
Vol 12 (2) ◽  
pp. 1508
Author(s):  
I Made Agus Wirawan ◽  
Retantyo Wardoyo ◽  
Danang Lelono
Keyword(s):  
Emotion Recognition ◽  
Eeg Signals ◽  
Data Set ◽  
Eeg Data ◽  
Network Method ◽  
Reduction Methods ◽  
Research Questions ◽  
Participant Characteristics ◽  
Diverse Data ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.

Lived Experiences of Young People who Live with Mental Healthcare Users in Limpopo, South Africa

2020 ◽  
Vol 22 (2) ◽  
Author(s):  
Mamakota Maggie Molepo ◽  
Faniswa Honest Mfidi
Keyword(s):  
South Africa ◽  
Mental Illness ◽  
Young People ◽  
Lived Experiences ◽  
Daily Living ◽  
Support Systems ◽  
Family Members ◽  
Mental Healthcare ◽  
Limpopo Province ◽  
Burden Sharing

Mental illness is more than just the diagnosis to an individual – it also has an impact on the social functioning of the family at large. When a parent or relative has a mental illness, all other family members are affected, even the children. The purpose of the study was to provide insight into the lived experiences of young people who live with mental healthcare users and the way in which their daily coping can be maximised. A qualitative, descriptive, phenomenological research was undertaken to explore and describe the lived experiences of young people who live with mental healthcare users in the Limpopo province, South Africa. Audiotaped, unstructured in-depth interviews were conducted with 10 young people who grew up and lived with a family member who is a mental healthcare user in their homes, until data saturation was reached. A content analysis was used to derive themes from the collected qualitative data. Four major themes emerged as features reflective of the young people’s daily living with mental healthcare user, namely psychological effects, added responsibilities, effects on school performances, and support systems. This study recommends that support networks for young people be established through multidisciplinary team involvement and collaboration and the provision of burden-sharing or a relief system during times of need. With the availability of healthy coping mechanisms and support systems, the daily living situations and coping of young people could be maximised, thereby improving their quality of life while living with their family members with mental illness.

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