Abstract
There is a large variation in EEG signals from human to human. Therefore, it is a tough task to create a subject-independent emotion recognition system using EEG. EEG is reliable than facial expression or speech signal to recognize emotions, since this cannot be self-produced. The proposed study aims to develop a subject-independent emotion recognition system with a benchmark database DEAP. In this work, deep neural network with simple architecture is used to classify low–high valence and similarly low–high arousal. EEG signals are nonstationary signals. In this, the stochastic properties as well as spectrum changes over time. For these types of signals, the wavelet transform would be suitable as features, hence, wavelet transform is used to obtain different frequency bands of EEG signals.
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Paul S, Mazumder A, Ghosh P, Tibarewala DN, Vimalarani G (2015) EEG based emotion recognition system using MFDFA as feature extractor. In: International conference on robotics, automation, control and embedded systems (RACE). IEEE, pp 1–5
Petrantonakis PC, Hadjileontiadis LJ (2010) Emotion recognition from EEG using higher order crossings. IEEE Trans Inf Technol Biomed 14(2)
Zhuang N, Zeng Y, Yang K, Zhang C, Tong L, Yan B (2018) Investigating patterns for self-induced emotion recognition from EEG signals. Sensors 18(3):841
Koelstra S, Muhl C, Soleymani M, Lee J, Yazdani A, Ebrahimi T, Pun T, NIjhilt A, Patras I (2012) Deap: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 3(1):18–31
Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161–1178
Bradley MM, Lang PJ (1994) Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Exp Psychiatry 25(1):49–59
Zhang J, Chen M, Zhao S, Hu S, Shi Z, Cao Y (2016) ReliefF-based EEG sensor selection methods for emotion recognition. Sensors 16(10):1558
Jirayucharoensak S, Pan-Ngum S, Israsena P (2014) EEG-based emotion recognition using deep learning network with principal component based covariate Shift adaption. World Sci J 2014(627892):10
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This study uses publically available DEAP database.
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Pandey, P., Seeja, K.R. (2019). Subject-Independent Emotion Detection from EEG Signals Using Deep Neural Network. In: Bhattacharyya, S., Hassanien, A., Gupta, D., Khanna, A., Pan, I. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-13-2354-6_5
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DOI: https://doi.org/10.1007/978-981-13-2354-6_5
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