key: cord-0956798-p6dszm76
authors: Phutela, Nishtha; Relan, Devanjali; Gabrani, Goldie; Kumaraguru, Ponnurangam; Samuel, Mesay
title: Stress Classification Using Brain Signals Based on LSTM Network
date: 2022-04-28
journal: Comput Intell Neurosci
DOI: 10.1155/2022/7607592
sha: 74903cbcc0967f579a7bd4302c87fb32fc77889b
doc_id: 956798
cord_uid: p6dszm76

The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals are frequently employed in stress detection research and are both inexpensive and noninvasive modality. This paper proposes a stress classification system by utilizing an EEG signal. EEG signals from thirty-five volunteers were analysed which were acquired using four EEG sensors using a commercially available 4-electrode Muse EEG headband. Four movie clips were chosen as stress elicitation material. Two clips were selected to induce stress as it contains emotionally inductive scenes. The other two clips were chosen that do not induce stress as it has many comedy scenes. The recorded signals were then used to build the stress classification model. We compared the Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) for classifying stress and nonstress group. The maximum classification accuracy of 93.17% was achieved using two-layer LSTM architecture.

Stress can be triggered by the change in the body's emotional response to various situations such as depression, anxiety, anger, grief, guilt, low self-esteem, etc. It can be classified as positive stress (eustress) or negative stress (distress) [1] . Stress is the root cause for a variety of mental health problems like depression and dementia and has an adverse effect on a person's performance [2] . Issues related to stress are rising exponentially worldwide; therefore, the detection and quantification of stress are of utmost importance [3] .

ere are different ways to measure stress levels. Traditionally, the stress level of an individual has been calculated only through self-reports [4] . Some standard questionnaires are available, and the answers filled by subjects to those questionnaires are mapped to some predefined scales. Each question is assigned some score based on the answer given, and the total score is calculated from all the questions answered [5] . Different standard scales are used in clinical settings through which stress can be quantified [6] [7] [8] but they are subjective indicators. Moreover, in developing countries, people do not prefer to go to psychologists and mental health clinics, due to the social stigmas associated with it. us, there is a need for a system that can automatically classify the subjects into stress and nonstress. It may also help to develop preventive measures, for instance, to make the people aware about their mental health [9] . Stress measurement based on questionnaires, facial expressions (blink rate of the eye, voice etc.), social media posts, etc. are either subjective or challenging to validate [10] [11] [12] [13] [14] .

It has also been observed that stress has a significant effect on specific physiological parameters like skin conductance, blood pressure, and brain signals [15, 16] . Various physiological signals acquired from different sources such as electroencephalography (EEG), Functional Magnetic Resonance Imaging (FMRI), and Positron Emission Tomography (PET) were used for the detection of stress [17] [18] [19] . Among these, EEG has gained acceptance in monitoring stress levels as it is noninvasive and nonexpensive and gives very high temporal resolution [20] .

ere are certain attributes (physical, physiological, etc.) that can help in classifying stress from nonstress individuals. But there is no direct metric for stress measurement.

Various attempts are made where the classification was performed using different features. But different features sets yield different results. Recently deep learning has been widely used in the domain of stress recognition through EEG [21, 22] as it can directly take input from raw data and identify the most prominent features automatically without any feature engineering and preprocessing [23, 24] . But according to [25] , although deep neural networks are capable of learning features, in order to yield high performance it is better to do feature extraction beforehand. Moreover, deep learning model is data hungry. us, to build a reliable stress detection system with sophisticated feature extraction tool is highly prized. At the same time it is not straightforward to know the optimal features which can classify the stress level with high accuracy. Moreover, the type and number of features to be extracted highly depend on the type of headband. EEG has advantages of low cost, high temporal resolution, and ease of use. It is one of the most used techniques for stress and other mental states' assessment [26, 27] .

To this end, we propose a stress classification system that utilizes the direct FFT signals provided by Muse headband for stress identification. We used inexpensive Muse headband for the acquisition of EEG signal as numerous studies have reported its applicability in identifying the brain activities of an individual [28, 29] . We build a simple stress classification system which uses minimum number of sensors. In literature, various systems are proposed which uses different set of features for stress classification but in our present work we used direct signal provided by the device which reduces the computational cost for calculating the features manually. In one of the previous works, authors used LSTM recurrent neural network for emotion classification with 4-channel EEG device [30] . To the best of our knowledge, we are the first to explore Long Short-Term Memory (LSTM) for stress classification using just 4 EEG electrodes. us, the major contributions of this paper are summarized as follows:

(i) Creating a data set of recorded EEG signals which were acquired while participants watched the video clip (as a stress elicitation material) to infer the stressed state of the subject. (ii) Building a model and comparing Multilayer Perceptron (MLP) and LSTM based architecture for the classification of stress data. e content in this paper is structured as follows: e literature review corresponding to this topic is given in Related Work section. e requisite background for understanding the paper is elaborated in Background section, the proposed methodology and experimental setup are described in Proposed Methodology section. Various metrics used to gauge the performance of the proposed model are mentioned in Performance Measures section. Results and Discussion section details out the results of the experiment conducted for the classification of stress. Conclusion and Future Work section contains some conclusive remarks and a few pointers on future work.

Notably, a psychologist can provide a huge amount of knowledge in identifying if a person is stressed. But in the absence of a psychologist, identifying features that are representative of stress from the collected EEG data becomes a challenge. Most of the work on stress detection has relied on various hand-crafted features [31, 32] , so there is a definite need to explore the area more. us, keeping in view the health problems that are associated with increasing stress levels, it becomes extremely important to identify that a person is stressed at an earlier stage.

Recently deep learning has been widely used in the domain of stress recognition through EEG [21, 22] . A detailed review of deep learning techniques for classification tasks using EEG signals is reported in [33] . e advantage of using deep learning is that it can directly take input from raw data and identify the most prominent features automatically without any feature engineering and preprocessing [23, 24] . As a result, the difficulty of selecting the best appropriate preprocessing algorithm and feature selection methods has been overcome, making this more applicable. But according to [25] , although deep neural networks are capable of learning features, it is better to do feature extraction beforehand.

is is because EEG signals contain noise and interference. Furthermore we need a lot of data for training purpose in building a deep learning model.

Stress can be detected in natural setting or controlled lab setting. e authors of [21] attempted to record the pattern of workers' brain waves at a construction site (natural setting) when they were under stress. eir aim was the early detection and mitigation of stress for the construction workers. To obtain the ground truth, the saliva of the workers was collected which contains a hormone called cortisol responsible to regulate stress. e authors conducted this study on 9 construction workers using 14 electrodes' mobile EEG device. e intrinsic signal artifacts were removed by using Independent Component Analysis (ICA) and the extrinsic signals artifacts were removed by using low pass filter, high pass filter, and notch filter with appropriate frequencies.

e authors have proposed a system which utilizes 14 electrodes and built a model using convolutional deep learning network and a fully connected deep neural network architecture for binary stress classification of stress. e accuracy reported by using fully connected DNN was 86.62%. eir DNN architecture consisted of 2 hidden layers with 83 neurons in the first layer and 23 neurons in the second hidden layer, while their approach reported an average increase in the stress classification accuracy as compared to their previous approach of using SVM by [34] . A major limitation of this study was smaller number of 2

Computational Intelligence and Neuroscience participants so it becomes hard to generalize the results. Also collecting saliva might not be favourable to the subjects under study. ey have also not specified if a medical practitioner was in their team to label the stress level based on collected saliva sample. Also, testing the internal validity in their experiments is a challenge because there is no mention of recording multiple sessions with same subject.

Under controlled lab setting, various stress elicitation material was used such as evoked emotional stress through multitasking [35] , Paced Auditory Serial Addition Test (PASAT) [36] , and Stroop Color Word Test (SCWT) [37] .

A stressed emotion dataset called Multimodal Dataset of Stressed Emotion (MuSE) has been presented in [22] to study the correlation between occurrence of stress and the presence of affect. ey have considered stress as one of the confounding factors in influencing the psychological state of a person. ey have collected data from 28 students during the final exams and after the exams period to create datasets for stress and nonstress, respectively. e experiment is comprised of a series of the following events: emotional stimuli presentation, video watching, and emotionally evocative monologues. Perceived Stress Scale (PSS) was used to get self-reported scores of stress and Self-Assessment Manikin (Sam) was used for emotional assessment. ey used a paired t-test to infer that the average PSS scores obtained from the two groups were significantly different. A considerably large recording of 45 minutes was used. e novelty in their experiment design was the use of different emotional elicitation materials across all sessions, even though the emotional dimension being captured was the same. ey have used various unimodal deep neural networks and also a multimodal ensemble for the modelling valence and activation. ey have segmented the video dataset with a window size of 1 second and an overlap of 0.5 seconds. Out of the diverse set of features that have been used in this work, the visual and physiological modalities perform the best for stress elicitation while being influenced emotionally. e reported accuracy from their work is 70%. Other datasets available for stress detection include the one in [38] ; it is a real world biometric dataset collected from nurses working in a hospital at the time of COVID-19. e physiological variables measured in this dataset include EDA, heart rate, GSR, and accelerometer reading. WESAD is another publicly available dataset collected in a controlled lab setup. It contains physiological and motion related data of 15 participants [39] . Various machine learning algorithms have been used to differentiate stressed, neutral, and amused emotion. Authors in [40] have introduced another large scale dataset of stress using physiological signals. Reference [41] is a multidomain social media dataset for identifying stressed state of an individual. e use of stress elicitation material in the proposed work is inspired by work by authors in [31] , who use EEG, arousal, and valence dimensions to measure stress during video watching. Videos contain audio as well as visual stimuli that have more effect on the brain as compared to using a single stimulus. e application of LSTM network for classification of brain signals has been reported by [42] [43] [44] .LSTM with attention mechanism has been used by [45] to develop crosssubject generalized solution for classifying limb (hand) movements using EEG. ey have used frequency as well as time based features as input to LSTM network and obtained an accuracy of 83.2%. In [46] the authors reported the highest accuracy of 92.8% in context of driver stress classification tasks (at different weather conditions and other ambient factors) using single physiological signal, i.e., electrocardiogram (ECG) signal. ey used LSTM and CNN to detect driver's stress.

is section presents the relevant background details required for understanding of the proposed stress classification model.

Perceptrons are the type of feedforward neural networks that are widely used because they operate fast, can efficiently work on small training data sets, and are easy to implement. A typical MLP architecture consists of an input layer, series of hidden layers, and an output layer. e input layer consists of neurons equivalent to the number of features in the input data. e hidden layer processes the information selectively from input layer. It accomplishes this by associating weights and biases with the input features.

ere is no fixed rule to obtain the number of neurons in the hidden layer. It is a hyperparameter and has to be tuned with multiple trials. e output layer consists of the number of neurons depending on the classification task. For instance, if the problem consists of a binary classification task, then the output layer will contain only one neuron.

Schmidhuber in 1997. e special feature about this kind of neural networks that differentiates them from Recurrent Neural Networks (RNNs) is that they can learn long term dependencies. It is one of the best algorithms to work with sequence data along with an additional feature of having a memory element [47] . is memory element enables LSTM to remember the previous sequence of steps. It overcomes the difficulty of vanishing gradient, faced with RNN by a slight modification in the structure. Figure 1 shows the elementary architecture of a cell in LSTM [48] . e enabler of this cell is the parallel line shown in the upper part of Figure 1 (C t ).

LSTM can let selected information to flow through it with the help of this cell state. is feature comes with the help of three logic gates. Each of these gates gets input from the sigmoid activation function. e forget gate (f t ) is the first gate that selects the information that needs to be discarded from the cell. e equation for the forget gate is

e input gate is the second gate; its functionality can be explained in two parts (i t ) and (c t ):

e first part is Computational Intelligence and Neuroscience explained through (2) which involves a sigmoid function that computes any update in the previous values. e second part involves the tanh function as shown in (3) which creates a vector of new updated values.

en the state of the old cell C t−1 is replaced by the new cell state by removing the information generated by the forget gate in (1) . C t in (4) denotes the updated cell state.

Finally, the output is surpassed from a sigmoid layer and then a tanh layer to classify.

After the above steps, the cell state is updated. Lastly, output of current state is computed by taking the values of updated state of the cell state and also values from the sigmoid layer that determines the components of the cell state that need to be included in the output. e terminologies used in the above equations are described as below:

(1) e activation function s is sigmoid that suppresses the values in the interval (0, 1). It may be noted that the last sigmoid layer will classify the data into stressed or nonstressed group.

We used Interaxon Muse brain sensing headband with 4-channel EEG devices to acquire brain signals. It is a low cost device as compared to the medical EEG devices used by doctors. is headband is easy to adjust, does not consist of any wires, and does not need medical supervision. e Muse headband consists of four dry electrode channels (TP9, AF7, AF8, and TP10) working at global standard of 10-20 coordinates. AF7 and AF8 are the two forehead electrodes while TP9 and TP10 are two ear electrodes (see Figure 2 ). e device outputs the brain waves into various frequency ranges, namely, (i) delta, (ii) theta, (iii) alpha, (iv) beta, and (v) gamma.

Delta (0-4 Hz) is the default brain wave signal. ese signals are observed when we sleep, in clam state, and when a person is in comma. ese are affected in case of serious brain injury. eta (4-8 Hz) is a transitory brain wave between the slower delta and the comparatively faster intermediate brain waves, i.e., beta and alpha. It is said to be associated with creativity. Beta/ eta ratio is useful to determine the activeness of a person. eta reflects activity from the limbic and hippocampal regions. Alpha (8) (9) (10) (11) (12) is associated with meditation. Alpha has been associated with wakeful mindfulness. ey are strongest over occipital (back of the head cortex) and frontal cortex. Beta waves (>12 Hz, 13-21 Hz), on ther hand, are associated with high analytical thinking. A lot of beta activity happens in the frontal lobe region (AF7 and AF8). Frontal lobe is the area connected with executive function which is in turn responsible for figuring things out. Beta is highly associated with high performance and anxiety. Gamma waves are very fast oscillations (>30 Hz, 31-80 Hz) and are associated with higher level information processing like integrating thoughts.

For this experiment, signals from 40 subjects were acquired and out of these the data from 5 subjects were corrupted because the connection between electrodes and scalp was loose. is was identified during the manual inspection as the signal has NAN value for these subjects. Finally, 35 subjects were selected (18 males and 17 females) from the age group between 23 and 55 years. All the subjects were healthy and did not have any kind of neurological disorder. For each subject EEG signals were recorded. e subjects of our experiment were instructed not to consume any caffeine product at least 12 hours prior to the start of the experimental process because caffeine is found to interfere with brain activity [49] . e data used in the analysis has been collected from participants wearing the Muse headband as shown in Figure 2. e headband was adjusted to the comfort of the participant. Subsequently, they were shown four movie clips from the list shown in Table 1 . e use of stress elicitation material in proposed work is inspired by work by authors in [31] , who use EEG, arousal, and valence dimensions to measure stress during video watching. ese authors have used the circumplex model of affect. is model maps stress to high arousal and low valence. Stress causes high arousal because the mind is activated, but the activation is not pleasant, so the valence is low. Table 1 contains the information about the clips shown. ese clips were specifically chosen from Indian film clips because the subjects were able to relate better to these clips. A buffer of 2 minutes was incorporated after each clip.

is was done to relax the participant from the effect of the stressed and nonstressed videos. e venue for the experiment was an isolated room. e participants were asked to switch off all electronic devices present with them so that there is minimum interference from these devices on the EEG signals [50] .

While watching the video clip, the participant's EEG signals were recorded. After watching each movie clip, the participant filled an assessment form to infer the level of stress induced by each of these clips. e subjects were asked to complete the State Anxiety questionnaire [8] . State Anxiety is a multiscale questionnaire which we have used to test if the subject has experienced stress after watching the video clip. It has total 20 items. Each of these items is used to infer the feeling of the subject at the current moment. e responses of these questions were taken on a 4-point Likert Scale (1-Not at all stressed, 2-Some what stressed, 3-Moderately stressed, 4-Very much stressed). e answers from all the respondents were evaluated according to the standard scoring key of State Anxiety scale. e scores of this questionnaire were generally higher after watching the stress inducing videos and lower after watching the nonstressed videos. is data was used as a ground truth to label each instance of EEG recording from the respective person. e procedure followed was in accordance with Helsinki declaration. Also, the participants were informed about the procedure in advance and a consent form was signed by them before starting the experiment.

It may not be necessary that every participant will get stressed after watching the stress video enlisted by the authors; it depends on an individual's stress coping ability [51] . e score of State Anxiety form varies from 20 to 80. We calculated the average scores of participants from the questionnaire, similar to work performed in [32] . e participants whose score was greater than the average score were categorized in the stress group and the others in nonstressed group. us, the authors explicitly used average score in the questionnaire as the threshold. Higher scores correlate with greater anxiety. e records in the data set were labelled as stressed if the score obtained in the State Anxiety scale is greater than or equal to 50 and nonstressed if the score is less than 50. Authors in [52] Computational Intelligence and Neuroscience of arousal and valence was explained to the subjects and they were asked to rate the video in terms of arousal and valence. e range of values of arousal and valence for identifying the stressed and nonstressed states were similar to those used by [31] . e data of the participants which did not represent stressed and nonstressed behaviours (in terms of arousal, valence, and State Anxiety) corresponding to the stressed and nonstressed stimuli was discarded. is was done to ensure that the ground truth of the stress classification model is correct. ere were 2 such subjects so their data was discarded.

e LSTM architectures were built-in Keras 2.0.9 using TensorFlow backend in Python 3.6. Using LSTM with Keras requires the input in three dimensions (samples, time steps, and features). Our long univariate time series data sequence was reshaped into smaller segments and then fed into Keras. ese segments/subsequences can be overlapping or nonoverlapping. In our work, we split our long time series data into overlapping subsequences to increase the number of training samples.

is also helps to capture the dependence between individual subsamples of data. A smaller window size leads to the model getting trained faster and makes the model more robust and able to capture more information from individual slices of a single sequence of data. As EEG is a fast and dynamic signal which changes within a short duration of time, it is important to process the data in small chunks [53] .

us we took a small window size.

We used the LSTM network for the binary classification of stress due to its associated advantages. LSTM is used for sequence classification problems and has ability to extract significant temporal information from physiological signals [30, 54] . Moreover, the LSTM network makes predictions based on the individual time steps of the input sequence data. e dataset was divided into similar length sequences, which is an important step while the training process as input sequences should be of the same length.

us, the length of the EEG recording for each of the trials was 80 seconds. Each second of the recording has 50 data points. So a total of 4000 data points exist for each of the trials. A window size of 20 was selected with an overlap of 50% to break down this sequence into smaller segments.

A minibatch gradient descent algorithm was trained on these smaller segments with a batch size of 32. Minibatch gradient descent is the most common implementation of gradient descent. e frequency of update process after each iteration is faster as compared to batch gradient descent. is helps to avoid local minima by giving a robust convergence.

Minibatch sizes, commonly called "batch sizes" for brevity, are often tuned to an aspect of the computational architecture on which the implementation is being executed, such as a power of two that fits the memory requirements of the GPU or CPU hardware like 32, 64, 128, 256, and so on. e smaller values of this minibatch size give a learning process that converges quickly at the cost of noise in the training process. Large values give a learning process that converges slowly with accurate estimates of the error gradient. A good default value for batch size is 32 [55] . Reference [56] has also concluded that information on emotions contained in the EEG signal may be better described in shorter time segments.

We started with one LSTM layer (LSTM 1) containing 8 neurons and gradually included a second LSTM layer (LSTM 2) containing 16 neurons and subsequently another LSTM layer (LSTM 3) containing 24 neurons. ere was not much difference in the accuracy obtained between the LSTM 2 and LSTM 3 model. us we concluded LSTM 2 model was sufficient to classify EEG signals as the accuracy does not improve further, which is in accordance with the observation made in [25] .

Our model also contained a fully connected dense layer for classification [57] . Because deep learning networks tend to overfit, we also use a dropout layer to avoid the model learning noise [58, 59] . We used sigmoid activation function and one neuron in the output layer since we were doing binary classification (stressed and nonstressed). Since our training data contains 20 features/ signals (4 signals (corresponding to signal from 4 regions: TP9, AF7, AF8, and TP10) for each of the 5 brain frequency bands) as shown in Figure 3 , 20 neurons were used in the input layer.

Also, the state-of-the-art Adam optimizer was used to regulate the change in step size during the learning process. To evaluate how well the trained model has performed, binary cross entropy loss function was used because we had only one neuron in the output layer. e values of parameters obtained after hyperparameter tuning have been listed in Table 2 . e values of these hyperparameters have been chosen based on cross validation. e parameters which performed best have been chosen.

e following performance measures have been identified to evaluate the performance of the stress classification framework. e count of true positive cases is indicated by TP. e count of true negative cases is represented by TN. False positive cases are shown with FP and false negatives with FN. is metric gives information about actual labels for the data and the labels that are obtained through the classification model used. e diagonal elements of this matrix give the correctly classified records and the offdiagonal elements give the misclassified records.

e accuracy of the classifier is calculated based on Accuracy � TP + TN TP + TN + FP + FN × 100. Computational Intelligence and Neuroscience is metric gives an estimate of the fitness of the classifier used.

is metric maps the actual nonstressed instances to those identified by the classifier. is in turn is calculated using

e motive of calculating precision is to find the total number of correct positive predictions from the total number of positive predictions using

is metric corresponds to the number of samples correctly classified as being stressed. Mathematically, it is calculated using

5.6. F1-Score. is particular metric uses the harmonic mean of precision and sensitivity and is preferable when the dataset is unbalanced as the minority class also carries significant amount of information. It is calculated using Figure 3 . After collecting data, it is preprocessed into a form compatible with LSTM architecture. e detailed working of LSTM has already been described in Section 3.

e classification of the mental stress of the participants into stressed and nonstressed categories was achieved with the help of MLP and LSTM. State-of-the-art parameter values ( Table 2) were used for the implementation of MLP and LSTM. ese algorithms were run on the same system to reduce experimental error. e performance of the stress classification model is measured with the help of the following training-testing data partitions: 50-50, 60-40, 70-30, and 10-fold cross validation. e data was split into various proportions for testing and training in all these techniques. 10-fold cross validation was used to introduce randomisation into the training and test set choice. For each of the validation techniques used, Table 3 shows a confusion matrix consisting of two rows and two columns corresponding to each of the classifiers used.

e diagonal elements of this matrix indicate the instances in the dataset that were correctly classified. e nondiagonal elements constitute the wrongly classified instances. e high values of the diagonal elements indicate that our model is correctly able to differentiate stressed and nonstressed classes. As depicted in Table 4 , LSTM 2 gives the average accuracy of 91.96% and maximum accuracy of 93.17% using 10-fold cross validation, which is much higher than the accuracy obtained through MLP.

is result demonstrates the capabilities of LSTM to remember long term information from the sequential data. Figure 4 denote the train and test accuracy and loss obtained by deploying LSTM 2 model on our data. Also, Table 5 illustrates the other performance metrics used which show the robustness of the proposed approach. Higher recall and precision of our model showed that it gives less false negatives and false positive, respectively. High specificity denotes the true negative rate; i.e., a person classified as nonstressed was actually nonstressed. Table 6 shows the values of Mann-Whitney test computed on the results given by LSTM 2. ese denote that the improved accuracy results obtained with LSTM are highly significant. Table 7 shows the comparison of our proposed system with other state-of-the-art approaches available in the literature. It is evident from the comparison that our approach, which utilizes LSTM based model, is better in terms of classification accuracy. e work proposed by authors in [63] got a better stress classification accuracy using deep learning methods, but the number of subjects in their study was less than one-third of the number of subjects used in our proposed study. Also they have used saliva to elicit ground truth which is not easy because the saliva sample has to be sent to laboratory to extract the level of cortisol (stress hormone) present. It is the level of cortisol which further gives information about the stressed state of the person. e highest accuracy achieved Table 4 . ese results indicate that LSTM is a good candidate for classifying stress-related brain signals data. Classifier  MLP  LSTM 1  LSTM 2  50-50  4300  990  4700  600  5200  360  1700  2010  2108  1300  790  2650  60-40  3319  500  3794  402  4252  334  1481  1900  1006  1998  438  2176  70-30  2993  692  3150  490  3198  300  607  1108  450  1310  222  1680  10-fold cross validation  964  191  1045  140  1155  68  236  409  155  460 55 522 Computational Intelligence and Neuroscience

is study proposes a system for stress classification using EEG signal acquired from Interaxon Muse 4-channel commercially available headband device. e EEG signal was recorded from stress and nonstress subjects. e stress elicitation had been done using Hindi movie clips. Headband device provides the FFT signal which was directly used for classification task. We compared the MLP and LSTM model for classifying stress from nonstress. 35 participants took part in this experiment and watched film clips targeted to elicit the emotion of stress. e classification of stress was achieved with a maximum accuracy of 93.17% using LSTM 2 (with 2 LSTM layers). ese results demonstrated an improved performance over state-of-theart methods utilizing EEG signals. Moreover, it is important to mention here that direct comparison is not possible with the state-of-the-art methods due to difference in experimental setup, number of subjects, difference in the subjects, etc. e proposed stress classification method, from the best of our knowledge, is the first to observe the effect of video watching on mental stress using a commercially available EEG headband with 4 electrodes. is research work is a proof of concept to illustrate the applicability of an EEG headband to reliably identify the stressed state of a person. In future the authors will extend the work by conducting the experiment with more number of subjects.

Data Availability e data will be made available on request by the corresponding author.

ere are no conflicts of interest from the authors.

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