Emotion Analysis on EEG Signal Using Machine Learning and Neural Network
- URL: http://arxiv.org/abs/2307.05375v1
- Date: Sun, 9 Jul 2023 09:50:34 GMT
- Title: Emotion Analysis on EEG Signal Using Machine Learning and Neural Network
- Authors: S. M. Masrur Ahmed (1), Eshaan Tanzim Sabur (2) ((1) bKash Limited,
(2) BRAC University)
- Abstract summary: The main purpose of this study is to improve ways to improve emotion recognition performance using brain signals.
Various approaches to human-machine interaction technologies have been ongoing for a long time, and in recent years, researchers have had great success in automatically understanding emotion using brain signals.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Emotion has a significant influence on how one thinks and interacts with
others. It serves as a link between how a person feels and the actions one
takes, or it could be said that it influences one's life decisions on occasion.
Since the patterns of emotions and their reflections vary from person to
person, their inquiry must be based on approaches that are effective over a
wide range of population regions. To extract features and enhance accuracy,
emotion recognition using brain waves or EEG signals requires the
implementation of efficient signal processing techniques. Various approaches to
human-machine interaction technologies have been ongoing for a long time, and
in recent years, researchers have had great success in automatically
understanding emotion using brain signals. In our research, several emotional
states were classified and tested on EEG signals collected from a well-known
publicly available dataset, the DEAP Dataset, using SVM (Support Vector
Machine), KNN (K-Nearest Neighbor), and an advanced neural network model, RNN
(Recurrent Neural Network), trained with LSTM (Long Short Term Memory). The
main purpose of this study is to improve ways to improve emotion recognition
performance using brain signals. Emotions, on the other hand, can change with
time. As a result, the changes in emotion over time are also examined in our
research.
Related papers
- Speech Emotion Recognition Using CNN and Its Use Case in Digital Healthcare [0.0]
The process of identifying human emotion and affective states from speech is known as speech emotion recognition (SER)
My research seeks to use the Convolutional Neural Network (CNN) to distinguish emotions from audio recordings and label them in accordance with the range of different emotions.
I have developed a machine learning model to identify emotions from supplied audio files with the aid of machine learning methods.
arXiv Detail & Related papers (2024-06-15T21:33:03Z) - Leveraging Previous Facial Action Units Knowledge for Emotion
Recognition on Faces [2.4158349218144393]
We propose the usage of Facial Action Units (AUs) recognition techniques to recognize emotions.
This recognition will be based on the Facial Action Coding System (FACS) and computed by a machine learning system.
arXiv Detail & Related papers (2023-11-20T18:14:53Z) - Implementation of AI Deep Learning Algorithm For Multi-Modal Sentiment
Analysis [0.9065034043031668]
A multi-modal emotion recognition method was established by combining two-channel convolutional neural network with ring network.
The words were vectorized with GloVe, and the word vector was input into the convolutional neural network.
arXiv Detail & Related papers (2023-11-19T05:49:39Z) - Human Emotion Classification based on EEG Signals Using Recurrent Neural
Network And KNN [0.0]
emotion categorization from EEG data has recently gotten a lot of attention.
EEG signals are a critical resource for brain-computer interfaces.
EEG signals associated with good, neutral, and negative emotions were identified using channel selection preprocessing.
arXiv Detail & Related papers (2022-05-10T16:20:14Z) - Multimodal Emotion Recognition using Transfer Learning from Speaker
Recognition and BERT-based models [53.31917090073727]
We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities.
We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
arXiv Detail & Related papers (2022-02-16T00:23:42Z) - Stimuli-Aware Visual Emotion Analysis [75.68305830514007]
We propose a stimuli-aware visual emotion analysis (VEA) method consisting of three stages, namely stimuli selection, feature extraction and emotion prediction.
To the best of our knowledge, it is the first time to introduce stimuli selection process into VEA in an end-to-end network.
Experiments demonstrate that the proposed method consistently outperforms the state-of-the-art approaches on four public visual emotion datasets.
arXiv Detail & Related papers (2021-09-04T08:14:52Z) - Emotional EEG Classification using Connectivity Features and
Convolutional Neural Networks [81.74442855155843]
We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification.
The level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.
arXiv Detail & Related papers (2021-01-18T13:28:08Z) - A Novel Transferability Attention Neural Network Model for EEG Emotion
Recognition [51.203579838210885]
We propose a transferable attention neural network (TANN) for EEG emotion recognition.
TANN learns the emotional discriminative information by highlighting the transferable EEG brain regions data and samples adaptively.
This can be implemented by measuring the outputs of multiple brain-region-level discriminators and one single sample-level discriminator.
arXiv Detail & Related papers (2020-09-21T02:42:30Z) - Prediction of Human Empathy based on EEG Cortical Asymmetry [0.0]
lateralization of brain oscillations at specific frequency bands is an important predictor of self-reported empathy scores.
Results could be employed in the development of brain-computer interfaces that assist people with difficulties in expressing or recognizing emotions.
arXiv Detail & Related papers (2020-05-06T13:49:56Z) - Emotion Recognition From Gait Analyses: Current Research and Future
Directions [48.93172413752614]
gait conveys information about the walker's emotion.
The mapping between various emotions and gait patterns provides a new source for automated emotion recognition.
gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject.
arXiv Detail & Related papers (2020-03-13T08:22:33Z) - Continuous Emotion Recognition via Deep Convolutional Autoencoder and
Support Vector Regressor [70.2226417364135]
It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy.
Deep neural networks have been used with great success in recognizing emotions.
We present a new model for continuous emotion recognition based on facial expression recognition.
arXiv Detail & Related papers (2020-01-31T17:47:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.