Video Category Classification Using Wireless EEG

Mutasim, Aunnoy K; Tipu, Rayhan Sardar; Bashar, M. Raihanul; Amin, M. Ashraful

doi:10.1007/978-3-319-70772-3_4

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Researchers are now discovering the use of EEG signals for audiovisual content analysis. The most related research to our effort is done by Mutasim et al (2017), where researchers modeled the classification of three types of videos using EEG responses by testing different classifiers on extracted features at five-channel locations. Highest accuracy is achieved at AF8 channel position, but the proper justification of selecting a particular channel and feature is not justified.…”

Section: Key Observations and Findingsmentioning

confidence: 99%

Affective Video Tagging Framework using Human Attention Modelling through EEG Signals

Sharma

Dubey

Ranjan

2022

International Journal of Intelligent Information Technologies

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The explosion of multimedia content over the past years is not surprising, thus their efficient management and analysis methods are always in demand. The affectiveness of any multimedia content deals with analyzing human perception and cognition while watching it. Human attention is also one of the important parameters, as it describes the engagement and interestingness of the user while watching that content. Considering this aspect, a video tagging framework is proposed in which the EEG signals of participants are used to analyze human perception while watching videos. A rigorous analysis has been performed on different scalp locations and frequency rhythms of brain signals to formulate significant features corresponding to affective and interesting video content. The analysis presented in this paper shows that the extracted human attention-based features are generating promising results with the accuracy of 93.2% using SVM-based classification model which supports the applicability of the model for various BCI-based applications for automatic classification of multimedia content.

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Section: Key Observations and Findingsmentioning

confidence: 99%

Affective Video Tagging Framework using Human Attention Modelling through EEG Signals

Sharma

Dubey

Ranjan

2022

International Journal of Intelligent Information Technologies

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“…In terms of the number of electrodes, it is inferred from the state of the art that the standard number of EEG electrodes for emotion classification has not been established yet. Among others, the numbers of electrodes analysed for emotion classification are 64 electrodes [25,26], 14 electrodes [15,18], 8 electrodes [3,27] and 5 electrodes [17,28].…”

Section: Related Workmentioning

confidence: 99%

“…Likewise, different machine learning models have been employed to perform emotion classification, including multilayer perceptron [3], [12−14], support vector machine [1,15], k-nearest neighbor [16], adaptive Neuro-fuzzy inference systems [5], AdaBoost [17], dynamical graph convolutional neural network [18] and many others.…”

Section: Introductionmentioning

confidence: 99%

Emotional profiling through supervised machine learning of interrupted EEG interpolation

Yaacob¹,

Omar²,

Handayani³

et al. 2019

IJACR

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It has been reported that the construction of emotion profiling models using supervised machine learning involves data acquisition, signal pre-processing, feature extraction and classification. However, almost all papers do not address the issue of profiling emotion using supervised machine learning on the interrupted encephalogram (EEG) signals. Based on a preliminary study, emotion profiling on interrupted EEG signals produces low classification accuracy, using multilayer perceptron (MLP). Furthermore, lower emotion classification accuracy is produced from interrupted EEG signals with higher number of segments. Thus, the objective of this paper is to propose a technique and present the outcomes of handling interrupted EEG signals for emotion profiling. This is done by the suppression and interpolation of originally interrupted EEG signals at pre-process stage. As a result, emotion classification using MLP on interpolated data improves from 80.1% to 95%.

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