11institutetext: Computing Science and Engineering, Indraprastha Institute of
Information Technology, New Delhi 22institutetext: School of Computing Science
and Engineering, Vellore Institute of Technology, Vellore 33institutetext:
Centre for Biomedical Engineering, Indian Institute of Technology, New Delhi.

# Noise removal methods on ambulatory EEG: A Survey

Sarthak Johari 11 Gowri Namratha Meedinti 22 Radhakrishnan Delhibabu 22 Deepak
Joshi 33

###### Abstract

Over many decades, research is being attempted for the removal of noise in the
ambulatory EEG. In this respect, an enormous number of research papers is
published for identification of noise removal, It is difficult to present a
detailed review of all these literature. Therefore, in this paper, an attempt
has been made to review the detection and removal of an noise. More than 100
research papers have been discussed to discern the techniques for detecting
and removal the ambulatory EEG. Further, the literature survey shows that the
pattern recognition required to detect ambulatory method, eye open and close,
varies with different conditions of EEG datasets. This is mainly due to the
fact that EEG detected under different conditions has different
characteristics. This is, in turn, necessitates the identification of pattern
recognition technique to effectively distinguish EEG noise data from a various
condition of EEG data.

###### Keywords:

Ambulatory EEG, Emotion Recognition, Gated Recurrent Unit, Machine learning,
Deep Learning, Temporal Dependencies, Preprocessing, Feature Extraction,
Predictive Modeling, Emotional States.

##  1 Introduction

Electroencephalography (EEG) is a non-invasive neuroimaging technique that
records electrical activity in the brain through electrodes placed on the
scalp. EEG signals are widely used in clinical and research settings to study
brain function and diag-nose neurological disorders. However, EEG signals are
often contaminated by various sources of artifacts, which can significantly
affect the quality of the data and reduce the accuracy of any analyses or
inferences made.

Common sources of artifacts in EEG signals include eye movements, muscle
activity, and environmental noise. Eye movement artifacts are particularly
challenging because they are often highly correlated with the underlying EEG
activity and can be difficult to distinguish from it. The presence of these
artifacts can interfere with the interpretation of EEG data and limit its
clinical and research applications.

To address this challenge, researchers have developed various techniques for
artifact removal in EEG signals. Traditional methods, such as filtering and
averaging, have been used for artifact removal but have limitations in their
effectiveness. Filtering techniques can remove some types of artifacts, but
they can also lead to a loss of signal information. Averaging techniques can
reduce the effects of random noise but are less effective for removing non-
random artifacts such as eye movements.

More advanced techniques have been developed in recent years to improve
artifact removal in EEG signals. Independent Component Analysis (ICA) is a
statistical tech-nique that has been widely used in EEG artifact removal. ICA
involves separating the observed signals into statistically independent
components, some of which correspond to noise sources and can be removed. ICA
has been shown to be effective in removing eye movement artifacts from EEG
data.

Blind Source Separation (BSS) is a more general framework that can be used to
separate any mixture of signals into their underlying sources. BSS has shown
particular promise in removing eye movement artifacts from EEG data by
exploiting the spatial and temporal characteristics of these artifacts.

Deep learning methods have also been employed for artifact removal in EEG sig-
nals. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks
(RNNs) are two types of deep learning methods that have been successfully
applied in EEG artifact removal. These methods can learn complex patterns in
the data and can be trained to remove specific types of artifacts.

Hybrid methods that combine different approaches have also been developed to
improve artifact removal performance. For example, some methods combine ICA
and machine learning techniques to achieve more effective artifact removal.
Despite the progress made in artifact removal techniques, the choice of method
depends on the specific type of artifact and characteristics of the data being
analyzed. Future research may continue to explore the development of more
advanced and effec-tive artifact removal techniques to further improve the
quality and accuracy of EEG data analysis.

##  2 Background Work

Noise removal methods for ambulatory EEG data encompass a range of approaches,
each offering its own set of advantages and limitations. One popular method is
Empirical Mode Decomposition (EMD), which adaptively decomposes the EEG signal
into ordered components called intrinsic mode functions. EMD is advantageous
as it is data-driven and does not require prior knowledge of basis functions.
However, it should be noted that EMD can be computationally intensive, which
may pose challenges in real-time applications or large-scale datasets.

Another method, Wavelet-Based Vector Boosting Estimation (Wavelet-VBE), has
shown effectiveness in removing additive white Gaussian noise commonly
encountered in ambulatory EEG recordings. This technique exploits the wavelet
transform and employs boosting algorithms for noise estimation and removal.
While Wavelet-VBE excels in tackling speciﬁc types of noise, it may not
perform optimally for other noise sources present in the data.

For mitigating muscle artifacts, Empirical Mode Decomposition combined with
Moving Average Filtering (EMD-MAF) has proven to be a suitable approach. EMD-
MAF separates the EEG signal into its intrinsic mode functions and applies a
moving average ﬁlter to reduce muscle artifacts. However, it is important to
note that EMD-MAF may not be as effective in addressing other forms of noise
that may be present in the data. Generative Adversarial Networks (GANs) have
gained attention for their ability to generate and denoise signals. GAN1 and
GAN2 have shown promise in denoising ambulatory EEG data, with GAN1 being
effective for muscle artifacts and baseline wander, while GAN2 excels at
removing additive white Gaussian noise. GANs leverage the power of deep
learning and adversarial training to generate denoised EEG signals. However,
GANs may require extensive computational resources and large amounts of
training data to achieve optimal denoising performance.

Denoising Least Squares Regression (DLSR) is a technique that combines least
squares regression with sparse representation to remove noise from EEG
signals. DLSR has demonstrated effectiveness in mitigating power-line
interference and additive white Gaussian noise. However, its performance may
vary for other types of noise. The Adaptive Kalman Filter (AKF) is another
approach used for noise removal in ambulatory EEG data. AKF utilizes the
principles of the Kalman ﬁlter to estimate and remove noise components from
the EEG signal. It has shown promising results for addressing additive white
Gaussian noise and muscle artifacts.

Empirical Wavelet Transform (EWT) has been successful in tackling power-line
interference commonly encountered in EEG recordings. By decomposing the signal
into empirical wavelets, EWT provides an effective means of removing power-
line noise. However, its performance may be less optimal for other types of
noise. Fully Convolutional Network-based Denoising Autoencoder (FCN-based DAE)
is a deep learning approach that leverages autoencoders and convolutional
neural networks to denoise EEG signals. FCN-based DAE has shown promise for
composite noise removal in ambulatory EEG data. However, it should be noted
that this method may require signiﬁcant computational resources due to the
complexity of the neural network architecture.

Discrete Wavelet Transform (DWT) has proven to be effective in denoising
ambulatory EEG data, particularly for addressing composite noise. By
decomposing the signal into different frequency sub-bands, DWT enables
targeted noise removal. However, its efﬁcacy may vary for different noise
types.

The choice of noise removal method for ambulatory EEG data depends on the
speciﬁc noise sources present and the desired denoising objectives. Each
method has its strengths and limitations, and researchers should carefully
consider the characteristics of the noise and the trade-offs associated with
each technique in order to select the most appropriate approach for their
speciﬁc application.

##  3 Related Work

Yuheng Feng et.al addressing the problem of muscle artifact removal from EEG
signals, propose a new method that combines singular spectrum analysis (SSA)
and canonical correlation analysis (CCA) algorithms to address this issue,
particularly in the case of ambulatory EEG devices with a limited number of
channels [1]. The study’s meth-ods involve a simple yet effective scheme that
is extended to the few-channel case by additional combining and dividing
operations to channels. The proposed framework is evaluated on both semi-
simulated and real-life data, and its performance is compared with state-of-
the-art methods. The results demonstrate that the proposed approach
outperforms other methods in both single-channel and few-channel cases. In
[2], Mad-dirala et.al addresse the problem of motion artifacts in
electroencephalogram (EEG) signals. The authors propose a method based on
singular spectrum analysis (SSA) to address this issue for single-channel EEG
signals.The study’s methods involve decom-posing the original EEG signal into
several sub-signals using SSA, followed by selecting and reconstructing the
sub-signals that contain relevant information while excluding the ones with
motion artifacts. The authors evaluate their proposed method using both
simulated and real-life EEG signals and compare its performance with state-of-
the-art methods.The results demonstrate that the proposed approach outperforms
other methods in terms of artifact removal while preserving the original EEG
signal’s characteristics. The authors conclude that their method is effective
and can be applied in real-world scenarios where motion artifacts are a
prevalent issue in single-channel EEG recordings.

ORegan et.al propose a method for automatic detection of these artifacts by
using both EEG and gyroscope signals [3]. The study’s methods involve
recording EEG and gyroscope signals simultaneously and developing an algorithm
that can detect and remove the artifacts caused by head movement. The authors
evaluate their proposed method on both simulated and real-life EEG data and
compare its performance with other state-of-the-art methods. The results
demonstrate that the proposed approach achieves high accuracy in detecting
head movement artifacts in EEG signals. The authors conclude that their method
is effective and can be used in real-world scenarios where head movement
artifacts are a prevalent issue in EEG recordings. Kafiul Islam et.al conclude
that there is no single method that is universally effective for detecting and
removing all types of artifacts from EEG signals and suggest that the choice
of method should be based on the specific type of artifact and the
characteristics of the EEG data. [4] The authors also suggest that a
combination of different methods may be necessary to achieve effective
artifact removal in EEG signals and emphasize the importance of considering
the computational complexity of the method, as well as its suitability for
real-world scenarios.

In [5], X. Chen et.al present a method for denoising muscle artifacts from
few-channel electroencephalogram (EEG) recordings using multivariate empirical
mode decomposition (MEMD) and canonical correlation analysis (CCA). Their
proposed method involves decomposing the EEG signals using MEMD and then
applying CCA to the resulting intrinsic mode functions (IMFs) to identify and
remove muscle arti-facts.They evaluated the effectiveness of their method by
applying it to both simulated and real EEG data, comparing it with other
state-of-the-art methods. The results demonstrate that the proposed method is
effective in removing muscle artifacts from few-channel EEG recordings while
preserving the underlying EEG signal.

In [6], Xiao Jiang,Gui-Bin Bian, and Zean Tian provide an overview of the var-
ious methods used for removing artifacts from electroencephalogram (EEG)
signals. The review describes the different methods used for artifact removal,
including filter-ing techniques, independent component analysis (ICA), and
blind source separation (BSS). The authors discuss the advantages and
disadvantages of each method and provide examples of studies that have used
these methods for artifact removal and highlight the challenges associated
with artifact removal, such as the need to balance artifact removal with
preservation of the underlying signal. They describe how some methods may
introduce artifacts of their own or remove relevant information along with the
artifacts. Garces Correa, E Laciar H D Patino and M E Valentinuzzi examines
the adaptive filtering method used for artifact removal in EEG signals [7].
The authors explain the advantages of using adaptive filters in a cascade
configuration, which they demonstrate is able to remove artifacts more
effectively than single filters alone. They present the results of their
study, which show that the adaptive filtering method is highly accurate in
removing various types of artifacts without distorting the underlying EEG
signal. The authors suggest that this method has the potential to improve the
accuracy of EEG signal analysis and enable more accurate diagnosis of
neurological disorders. They recommend further research to validate this
method on larger datasets and explore its potential for real-time artifact
removal.

Arad et al. introduces a novel technique for removing movement artifacts from
EEG data recorded during locomotion [8]. They evaluate the performance of
their approach using quantitative measures like root mean square error (RMSE)
and correlation coef-ficient, and show that it outperforms existing techniques
with significantly reduced RMSE and improved correlation coefficient values.
The authors also discuss the poten-tial of their method in clinical
applications, particularly in improving the accuracy of EEG-based diagnosis
and treatment of neurological disorders. The findings of this study suggest a
promising approach for removing movement artifacts from EEG data during
locomotion. Researchers and practitioners in this field can benefit from this
technique to enhance the quality of EEG data in challenging and dynamic
environ-ments. This could lead to a better understanding of brain activity
during movement and potentially improve the diagnosis and treatment of
neurological disorders.

Zhang, Huang and Wu introduce a new approach to remove artifact from EEG sig-
nals using a 1D- Residual Convolutional Neural Network (1D-ResCNN) and
describe their method in detail, which involves training a neural network to
learn the rela-tionship between clean EEG signals and their corresponding
noisy versions [9]. The authors evaluate their method using various
performance metrics and compare it to several existing techniques, showing
that the approach outperforms them in terms of signal-to-noise ratio, signal
quality index, and correlation coefficient.

Chinmayee Dora and Pradyut Kumar Biswal present a new method for remov-ing
electrocardiogram (ECG) artifacts from EEG signals [10]. The proposed method
utilizes continuous wavelet transformation and linear regression to remove the
ECG artifacts effectively.The authors evaluated their method on both simulated
and real EEG data and compared it with other state-of-the-art methods. The
results showed that the proposed method outperforms other methods in terms of
artifact removal efficiency, signal-to-noise ratio improvement, and
preservation of EEG signals’ spectral characteristics.

Figure 1: Evolution of EEG artifact removal methods

##  4 Dataset

The data used in this research was collected for a duration of 3 minutes from
two participants, consisting of one male and one female, for each emotional
state: positive, neutral, and negative. The participants were exposed to
specific stimuli designed to elicit the desired emotions. Additionally, six
minutes of resting neutral data were also recorded. The EEG data was captured
using a Muse EEG headgear equipped with dry electrodes. The following EEG
placements were utilized: TP9, AF7, AF8, and TP10. These electrode placements
are commonly used for capturing brainwave activity related to emotion
recognition.

The dataset used in this research is publicly available and can be accessed at
the following link: https://www.kaggle.com/datasets/birdy654/eeg-brainwave-
dataset-feeling-emotions. It provides access to the collected EEG data, which
includes the recordings for each participant in different emotional states, as
well as the neutral resting data.The dataset consists of time-series EEG
recordings, where each sample represents the electrical activity captured at
specific time intervals. The data is labeled according to the emotional states
experienced by the participants during the recordings. By utilizing this
dataset, the research aims to explore the effectiveness of various noise
removal methods on ambulatory EEG data and assess the performance of the GRU
algorithm in accurately predicting emotional states based on the recorded
brainwave activity.

##  5 Methodology

###  5.1 Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) have emerged as a powerful neural network
architecture for modeling sequential data. Unlike traditional feedforward
neural networks, RNNs have a unique ability to capture temporal dependencies
by introducing recurrent connections that allow information to flow from one
time step to another. This characteristic makes RNNs well-suited for tasks
involving sequential or time-series data analysis, including natural language
processing, speech recognition, and, in our case, emotion recognition from EEG
signals.

In standard neural networks, each input and output is treated as independent,
disregarding any contextual information. However, in many real-world
applications, such as sentiment analysis or language generation, understanding
the context of previous inputs is crucial for accurate predictions. RNNs
address this limitation by incorporating a hidden state, which acts as a
memory mechanism that retains information about past inputs and influences
future predictions.

The hidden state in an RNN serves as a crucial element for capturing long-term
dependencies within the sequential data. As the RNN processes each input in a
sequence, the hidden state is updated and passed to the next time step,
allowing the network to remember past information and utilize it to make
informed predictions. This recursive nature of RNNs enables them to model
complex temporal relationships and extract relevant features from sequential
data.

However, traditional RNNs suffer from the vanishing or exploding gradient
problem, which poses challenges in learning long-term dependencies. To address
this issue, advanced variants of RNNs, such as Gated Recurrent Units (GRUs)
and Long Short-Term Memory (LSTM) networks, have been developed. These
architectures introduce specialized gating mechanisms that selectively retain
or forget information, enabling the network to capture long-term dependencies
while mitigating the gradient vanishing or exploding problem.

In our research, we focus on the GRU algorithm as a variant of RNNs for
emotion recognition using EEG data. GRUs have gained attention due to their
simplified architecture, which consists of three gates: the update gate, reset
gate, and current memory gate. The update gate determines how much past
information should be propagated to future time steps, while the reset gate
controls the extent to which previous knowledge should be forgotten. The
current memory gate, often overlooked in discussions of GRUs, contributes non-
linearity and zero-mean normalization to the input, reducing the impact of
past data on future information.

By leveraging the GRU algorithm, we aim to predict emotional states by
analyzing EEG data collected from individuals exposed to various movie scenes
or stimuli. The utilization of GRUs offers an efficient approach to capture
temporal dependencies within the EEG signals, allowing us to explore the
effectiveness of this architecture in emotion recognition tasks.

###  5.2 Gated Recurrent Units (GRUs)

Gated Recurrent Units (GRUs) have gained significant attention as an
alternative architecture within the realm of Recurrent Neural Networks (RNNs).
GRUs address some of the limitations of traditional RNNs, such as the
vanishing or exploding gradient problem, while providing an efficient and
effective solution for capturing temporal dependencies in sequential data. In
this section, we will delve into the specifics of GRUs and their relevance to
our research on emotion recognition using EEG data.

GRUs are a type of RNN architecture that incorporates gating mechanisms to
regulate the flow of information within the network. Unlike the more complex
Long Short-Term Memory (LSTM) networks, GRUs have a simplified structure
consisting of three essential gates: the update gate, reset gate, and current
memory gate. This architectural design allows GRUs to strike a balance between
modeling long-term dependencies and computational efficiency. The update gate,
denoted as z, determines the extent to which the previous hidden state is
incorporated into the current state. It controls how much of the past
information should be carried forward to future time steps. By selectively
updating the hidden state, the GRU can adapt to different patterns in the data
and retain relevant context over time.

The reset gate, denoted as r, determines the extent to which the previous
hidden state influences the current state. It selectively resets or forgets
some of the previous knowledge, allowing the model to focus on relevant
features and adapt to changing patterns within the sequence. The combination
of the reset and update gates enables GRUs to capture and adapt to varying
dependencies within the data.

Another crucial component of GRUs is the current memory gate, often overlooked
in discussions of GRUs. It is a sub-component of the reset gate and plays a
vital role in introducing non-linearity and zero-mean normalization to the
input. This helps in reducing the impact of previous data on the current data
being propagated forward, ensuring that relevant information is preserved
while minimizing the interference from irrelevant or noisy signals.

Compared to a basic RNN, the workflow of a GRU is similar, with the primary
distinction lying in the internal functioning of each recurrent unit. By
leveraging the gating mechanisms, GRUs excel at capturing and modeling
temporal dependencies in the data, making them well-suited for tasks such as
emotion recognition from EEG signals.

In our research, we adopt the GRU algorithm as the core architecture for
predicting emotional states based on EEG data collected during exposure to
various movie scenes or stimuli. The GRU’s simplified yet powerful design
allows us to effectively model the temporal dynamics within the EEG signals,
contributing to the advancement of emotion recognition using EEG-based
approaches.

###  5.3 Preprocessing and Data Preparation

The preprocessing and data preparation stage is essential to ensure the
quality and suitability of the EEG data for accurate emotion recognition using
the GRU algorithm. We follow standard practices in EEG-based emotion
recognition to preprocess the data effectively. The raw EEG data collected
from participants wearing the Muse EEG headgear with dry electrodes is
subjected to artifact removal techniques such as independent component
analysis (ICA) or template matching algorithms. This step eliminates unwanted
noise and artifacts, allowing us to focus on genuine EEG signals related to
emotional states.

Next, bandpass filters are applied to remove unwanted frequency components
while retaining the relevant frequency ranges associated with brainwave
activity. Normalization techniques, such as z-score normalization or min-max
scaling, are then employed to address amplitude variations between
participants or electrode placements.

Feature extraction techniques are utilized to capture relevant information
from the preprocessed EEG data. These features, including power spectral
density, signal entropy, or time-domain statistics, serve as input for the GRU
algorithm, enabling it to learn meaningful patterns and associations with
emotional states.

To ensure unbiased evaluation, the dataset is partitioned into training,
validation, and testing sets. Stratified or random partitioning techniques
maintain representative distributions of emotional states across the subsets.
This partitioning facilitates model training, hyperparameter optimization, and
unbiased evaluation of the GRU model’s generalization capabilities.

By implementing these preprocessing and data preparation steps, we enhance the
quality and suitability of the EEG data for subsequent analysis using the GRU
algorithm. The GRU model can effectively leverage the preprocessed data to
accurately predict emotional states.

###  5.4 Gated Recurrent Unit Algorithm

The Gated Recurrent Unit (GRU) algorithm is a variant of Recurrent Neural
Networks (RNNs) that excels at capturing long-term dependencies in sequential
data while mitigating the vanishing or exploding gradient problem. In this
section, we provide an overview of the GRU algorithm and its relevance to our
research on emotion recognition using EEG data. The GRU architecture is
designed to have a simplified structure compared to traditional RNNs and LSTM
networks. It consists of three fundamental gates: the update gate, reset gate,
and current memory gate. These gating mechanisms enable the GRU algorithm to
effectively model temporal dependencies within the data while maintaining
computational efficiency.

The update gate (z) determines the extent to which the previous hidden state
influences the current state. It controls the flow of information from past
time steps to future time steps, allowing the model to adapt and retain
relevant context over time. By selectively updating the hidden state, the GRU
algorithm can capture long-term dependencies and learn patterns within the
sequential data.

The reset gate (r) regulates the influence of previous knowledge on the
current state. It selectively resets or forgets certain information, enabling
the model to focus on relevant features and adapt to changing patterns within
the sequence. The combination of the update and reset gates empowers the GRU
algorithm to effectively model and adapt to varying dependencies within the
data.

The current memory gate, often overlooked in discussions of GRUs, plays a
critical role in introducing non-linearity and zero-mean normalization to the
input. By serving as a sub-component of the reset gate, it helps reduce the
impact of previous data on the current data being propagated forward. This
mechanism minimizes the interference of irrelevant or noisy signals and
ensures the effective transfer of information to future time steps.

Compared to a basic RNN, the GRU algorithm follows a similar workflow but
excels in its internal functioning within each recurrent unit. Leveraging the
gating mechanisms, GRUs excel at capturing temporal dependencies and
facilitating the prediction of emotional states from EEG data.

In our research, we employ the GRU algorithm as the core architecture for
predicting emotional states based on EEG data collected during exposure to
various movie scenes or stimuli. The GRU’s simplified yet powerful design
allows us to effectively model the temporal dynamics within the EEG signals
and contribute to the advancement of emotion recognition using EEG-based
approaches.

###  5.5 Architecture

The architecture in Fig 2., used in our research leverages the Gated Recurrent
Unit (GRU) algorithm for emotion recognition based on EEG data. This
architecture comprises an InputLayer, GRU layer, Flatten layer, and Dense
layer. The configuration of this architecture is as follows:

Figure 2: Model Architecture

  1. 1

_InputLayer_ : The InputLayer serves as the entry point for the EEG data into
the neural network. It defines the shape and format of the input data,
aligning with the preprocessing steps and feature extraction performed on the
EEG data. The input layer represents the initial stage of information flow in
the neural network.

  2. 2

_GRU Layer_ : The GRU layer is the core component of the architecture and
employs the Gated Recurrent Unit algorithm. It processes the sequential EEG
data, capturing temporal dependencies and extracting relevant features for
emotion recognition. The GRU layer’s hidden state retains information from
previous time steps and influences the predictions made by subsequent layers

  3. 3

_Flatten Layer_ : Following the GRU layer, the Flatten layer is applied to
transform the multi-dimensional output of the GRU into a one-dimensional
vector. This flattening operation enables the subsequent layers to receive a
flat input, facilitating compatibility with traditional fully connected
layers.

  4. 4

_Dense Layer_ : The Dense layer, also known as the fully connected layer,
receives the flattened output from the preceding layer. It serves as a
powerful learning component, responsible for mapping the extracted features to
the emotional states being predicted. The dense layer consists of multiple
interconnected neurons, and each neuron contributes to the final emotion
classification based on learned weights and biases.

This architecture efficiently processes the preprocessed EEG data through the
GRU layer, capturing temporal dynamics and learning meaningful patterns
related to emotional states. The subsequent flatten and dense layers allow for
feature extraction and final classification, respectively.

##  6 Results and Analysis

The deep learning accuracy details of the GRU model on the validation set are
as follows: loss - 3.4356e-09 and accuracy - 1.0000. These impressive results
indicate that the GRU model achieved perfect accuracy in predicting emotional
states based on the EEG data. The model’s ability to achieve such high
accuracy suggests its proficiency in capturing the temporal dynamics and
extracting meaningful features from the EEG signals. Additionally, we compare
the performance of the GRU model with other machine learning models using
their respective scores. The following table 1 summarizes the scores obtained
for various models:

Table. 1. Accuracies of different models

From the results, it is evident that the GRU model (95.46% accuracy) performs
competitively when compared to other machine learning models. Notably, the
Extreme Gradient Boosting Classifier achieves the highest score of 99.39%,
closely followed by the Random Forest Classifier with a score of 98.7%. The
Linear Support Vector Machine Classifier also demonstrates excellent
performance with an accuracy of 96.57%.

Figure 3: Accuracy and Loss curves

However, it is important to note that the GRU model showcases the advantage of
deep learning in capturing complex temporal dependencies and extracting
meaningful features from EEG data. Its accuracy of 95.46% suggests its
efficacy in predicting emotional states based on the EEG signals, showcasing
(Fib. 3) its potential for real-world applications.

Further analysis is required to delve into the strengths and weaknesses of
each model, considering factors such as computational complexity,
interpretability, and generalization capabilities. Additionally, a more
comprehensive evaluation using additional performance metrics like precision,
recall, and F1-score could provide deeper insights into the models’ overall
effectiveness.

Overall, the results demonstrate the effectiveness of the GRU algorithm in
accurately predicting emotional responses from EEG data. Its competitive
performance against other machine learning models supports the notion that
deep learning approaches, such as the GRU, can significantly contribute to the
advancement of emotion recognition tasks using EEG signals.

Figure 4: Confusion Matrix Analysis

The confusion matrix analysis (Fib 4) was conducted to evaluate the
performance of the machine learning models, including the GRU algorithm, in
predicting emotional states based on EEG data. The confusion matrix provides
insights into the model’s accuracy, precision, recall, and overall performance
by comparing predicted labels with actual labels. It helps identify potential
biases, misclassifications, and areas for improvement in the models’
predictions.

##  7 Discussion

The results obtained from our research demonstrate the effectiveness of the
GRU algorithm and other machine learning models in predicting emotional states
based on EEG data. The deep learning accuracy of the GRU model showed
remarkable performance, achieving perfect accuracy on the validation set. This
indicates the ability of the GRU model to capture temporal dependencies and
extract meaningful features from EEG signals, making it a promising approach
for emotion recognition tasks.

Comparing the performance of various machine learning models, we observed
competitive results across different models. The Extreme Gradient Boosting
Classifier achieved the highest score, closely followed by the Random Forest
Classifier, while the Linear Support Vector Machine Classifier also
demonstrated excellent performance. These findings highlight the importance of
choosing an appropriate model based on the specific requirements and
characteristics of the dataset.

The confusion matrix analysis provided valuable insights into the models’
predictions, allowing us to assess their accuracy and identify potential
biases or misclassifications. By examining the distribution of predicted
labels across different emotional states, we gained a deeper understanding of
the models’ strengths and weaknesses in capturing specific emotions. This
analysis can guide future improvements and refinements in the models’
performance.

##  8 Conclusion

In conclusion, Fib 5 shows the Mindgraph of the our study. Our research
explored the application of machine learning models, including the GRU
algorithm, for emotion recognition using EEG data. The GRU model demonstrated
exceptional performance, achieving perfect accuracy on the validation set,
highlighting its capability to capture temporal dynamics and extract
meaningful features from EEG signals.

Figure 5: Mindgraph of our study

Additionally, the comparison with other machine learning models emphasized the
competitive performance achieved by various approaches. The choice of model
should consider factors such as interpretability, computational complexity,
and generalization capabilities, based on the specific requirements of the
task.

The incorporation of the confusion matrix analysis provided valuable insights
into the models’ performance, aiding in identifying potential biases and areas
for improvement. By understanding the models’ strengths and weaknesses, future
research can focus on enhancing their accuracy and addressing specific
challenges related to emotion recognition from EEG data.

Overall, our findings contribute to the advancement of emotion recognition
using EEG signals and highlight the potential of the GRU algorithm and other
machine learning models in this field. Further research and exploration are
warranted to improve the robustness and generalizability of these models and
advance the understanding of human emotions through EEG-based approaches.

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## Appendix: Survey

Table. 2.  Survey