Real-Time Electroencephalography-Based Emotion Recognition System

This paper proposes parametric, general and effectively automatic real time classification method of electroencephalography (EEG) signals based on emotions. The specific characteristics of the high-frequency signals (alpha, beta, gamma) are observed, and then Fourier Transform, Features Extraction (mean, standard deviation, power) and the K-Nearest Neighbors (KNN) are employed for signal processing, analysis and classification. The proposed method consists of two stages for a multi-class classification and it can be considered as the framework of multi-emotions based on Brain Computer Interface (BCI). The first stage, the calibration, is off-line and it computes the signal processing, determines the features and trains the classification. The second stage, the real-time, is the test on new data. The FFT is applied to avoid redundancy in the selected features; then the classification is carried out using the KNN. The results show that the average accuracy results are 82.33% (valence) and 87.32% (arousal).

[1]  Riyanarto Sarno,et al.  Decision mining for multi choice workflow patterns , 2013, 2013 International Conference on Computer, Control, Informatics and Its Applications (IC3INA).

[2]  Ling Huang,et al.  Feature Extraction of EEG Signals Using Power Spectral Entropy , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[3]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.

[4]  Riyanarto Sarno,et al.  Real Time Fatigue-Driver Detection from Electroencephalography Using Emotiv EPOC+ , 2016 .

[5]  R. Sarno,et al.  MULTI-CRITERIA DECISION MAKING FOR SELECTING SEMANTIC WEB SERVICE CONSIDERING VARIABILITY AND COMPLEXITY TRADE-OFF , 2016 .

[6]  Olga Sourina,et al.  Real-Time EEG-Based Human Emotion Recognition and Visualization , 2010, 2010 International Conference on Cyberworlds.

[7]  Leontios J. Hadjileontiadis,et al.  Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis , 2010, IEEE Transactions on Affective Computing.

[8]  Priyanka Khatwani,et al.  A survey on different noise removal techniques of EEG signals , 2013 .

[9]  Dimas Anton Asfani,et al.  Significant preprocessing method in EEG-Based emotions classification , 2016 .

[10]  Dimas Anton Asfani,et al.  Classification of driver fatigue state based on EEG using Emotiv EPOC , 2016 .

[11]  Jon D. Morris Observations: SAM: The Self-Assessment Manikin An Efficient Cross-Cultural Measurement Of Emotional Response 1 , 1995 .

[12]  Brent Lance,et al.  Optimal Arousal Identification and Classification for Affective Computing Using Physiological Signals: Virtual Reality Stroop Task , 2010, IEEE Transactions on Affective Computing.

[13]  Riyanarto Sarno,et al.  Modified Time-Based Heuristics Miner for Parallel Business Processes , 2016 .

[14]  Franklin Pierce Wright Emochat: Emotional instant messaging with the Epoc headset , 2010 .

[15]  A.H. Basori,et al.  The development of 3D multiplayer mobile racing games based on 3D photo satellite map , 2008, 2008 5th IFIP International Conference on Wireless and Optical Communications Networks (WOCN '08).

[16]  Lei Guo,et al.  GA-SVM based feature selection and parameters optimization for BCI research , 2011, 2011 Seventh International Conference on Natural Computation.

[17]  Dimitrios Tzovaras,et al.  Automatic Recognition of Boredom in Video Games Using Novel Biosignal Moment-Based Features , 2011, IEEE Transactions on Affective Computing.

[18]  Qiang Wang,et al.  A Real-time Fractal-based Brain State Recognition from EEG and its Applications , 2011, BIOSIGNALS.