MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

World University root node

School of Computer Engineering & Technology

Engineering and Technology

4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022 - Proceedings

Impact of the Intelligent Control System in Assisting Humans Regarding Complex Information Processing

In recent times, the advancements in Brain-Computer Interface has not only been instrumental in achieving its fundamental purpose of aiding disabled people, but also in creating novel applications like playing games without physical controls or operating home appliances merely by the power of your brain. The electrical activity generated in the brain is measured by an EEG device after which the collected raw data undergoes through various steps, namely: Signal acquisition, Data Preprocessing, Feature Extraction, and Classification. This paper helps the reader in understanding the different algorithms and methods used in each of these processes. A detailed survey of various applications of BCI using different feature extraction and classification techniques is done. Finally, we have compiled all the current issues which hinder the efficiency of BCI systems. © 2019 IEEE.

ICCIDS 2019 - 2nd International Conference on Computational Intelligence in Data Science, Proceedings

A review of recent trends in EEG based Brain-Computer Interface

Stress has become a universal emotion that people experience in day to day life. In this paper, emotion detection is carried out using benchmark DEAP dataset by implementing a new feature extraction techniques named as Teager-Kaiser Energy Operator (TKEO) with a k-nearest neighbor (KNN), neural network(NN) and Classification Tree (CT) classifiers based on Electroencephalography (EEG). The study evaluates the performance and accuracy of emotion detection which is further used for stress identification as EEG gives good correlation with stress. Also, the present work compares the implemented TKEO feature extraction technique with Relative Energy Ratio (RER), and Kernel Density Estimation (KDE) techniques regarding accuracy. This paper demonstrates how the inclusion of TKEO enhances feature extraction and proves a promising approach to emotion detection as compared to other conventional techniques. The experimental results show that TKEO when used with KNN, NN, CT classifier gives comparatively higher accuracy than KDE and RER for channel 1 alpha band and channel 17 beta band for stress detection.

International Journal of Innovative Technology and Exploring Engineering (IJITEE)

Human Emotion Detection and Stress Analysis using EEG Signal

Movement is an important aspect of any human being's life therefore lack of it has adverse effect on their overall development. Physically disabled people unfortunately are devoid of this privilege. Mechanical wheelchairs provided some form of mobility but they relied on external help for moving around. Moreover, people with tetraplegia couldn't benefit from it due to loss of all four limbs. Brain Computer Interface (BCI) is construed as interacting with an electronic device solely with brain with the help of Electroencephalogram (EEG) Signals. This technology is applied to existing mechanical wheelchair to control it using brain to overcome the limitations of conventional wheelchair. This paper proposes cost effective application of BCI technology to aid in mobility of physically disabled people. The proposed system utilizes the voluntary eye blink as an event to trigger an action. An eye blink pattern is mapped to a context based physical action like moving the wheelchair in particular direction. © 2018 IEEE.

2018 3rd International Conference for Convergence in Technology, I2CT 2018

Innovative Approach to Control Wheelchair for Disabled People Using BCI

In our day-to-day life Emotions play an vital role. They help in identifying a human state of mind. Information about the human state of mind can significantly help in human-machine interaction and brain-computer interface. Some of the existing researchers have used speech, text, gesture or facial expressions for emotion recognition. However, these factors vary across culture and nation. Because of which, it is difficult to detect emotions more accurately. Hence, present work considers EEG signals for emotion recognition which not only ignores external factors but also helps to detect real emotions arising directly from our brain. A benchmark DEAP (Dataset for Emotion Analysis using Physiological signals) dataset is used for emotion investigation. A novel feature extraction technique called Frequency Cepstral Coefficient (FCC) is proposed to extract features from DEAP dataset. FCC technique is compared with Kernel Density Estimation (KDE) on DEAP dataset for feature extraction. These extracted features are then classified into two emotional states-happy and sad using K-Nearest Neighbor (K-NN) classifier. The selection of most appropriate and reliable method of feature extraction greatly helps for accurate classification. Number of experiments was conducted to evaluate the efficiency of feature extraction techniques. The experimental results show that KDE gives 80% accuracy and FCC outperforms it by achieving 90% accuracy on DEAP. © 2017 IEEE.

2017 Innovations in Power and Advanced Computing Technologies, i-PACT 2017

A novel approach for analyzing human emotions based on electroencephalography (EEG)

At present Brain Computer Interface (BCI) technology plays an important role in the field of biomedical. BCI can be used to identify human stress based on various emotion states - happy, sad, disgust, fear. Various techniques such as Neural networks (ANN), statistical methods, autoregressive model (AM), mixture of densities approach, independent component analysis (ICA), time-frequency analysis (TFA), bayes quadratic, hidden markov model (HMM) and linear discriminate analysis (LDA) have been used for EEG signal analysis. There are some issues which are being focused on while extracting features are -handling irrelevant and redundant features require more computation for decomposition of EEG signals. And the paper is considering following issues while carrying out classification - inability in processing complicated set of data, low performance when training data set is huge. This paper focuses on addressing above mentioned issues and appropriately analyzes various EEG signals which would in turn help us to improve the process of feature extraction and improve the accuracy in classification. In addition to acknowledging above problems, this paper proposes a framework which would be helpful in identifying human stress level and as a result, differentiate a normal or stressed person/subject.

Research Journal of Pharmaceutical, Biological and Chemical Sciences

Literature review of human stress analysis using EEG signal

This paper aims to proposed emotion recognition using electroencephalography (EEG) techniques. Recognizing emotion by using computers is becoming popular these days. This paper is based on calculating EEG signals and recognizing emotion from human brain activity. Electroencephalogram (EEG) signals are taken from the scalp of the brain and assessed in responds to several stimuli from the four basic emotions on the IAPS emotion stimuli. Features from the EEG signals are captured using the Kernel Density Estimation (KDE) and classified via the artificial neural network classifier to recognise emotional condition of the subject under test. Results are obtained to prove that the proposed modified KDE gives better results in terms of accuracy. Also, the proposed method gives better estimation of emotion of the subject from streaming EEG data by using the concept of cluster kernels. © 2015 The Authors. Published by Elsevier B.V.