A real-time wearable emotion detection headband based on EEG measurement

Abstract

A real-time emotion detection system based on electroencephalogram (EEG) measurement has been realised by means of an emotion detection headband coupled with printed signal acquisition electrodes and open source signal processing software (OpenViBE). Positive and negative emotions are the states classified and the Theta, Alpha, Beta and Gamma frequency bands are selected for the signal processing. It is found that, by using a combination of Power Spectral Density (PSD), Signal Power (SP) and Common Spatial Pattern (CSP) as the features, the highest subject-dependent accuracy (86.83 %) and independent accuracy (64.73 %) is achieved, when using Linear Discrimination Analysis (LDA) as the classification algorithm. The standard deviation of the results is 5.03. The electrode locations were then improved for the detection of emotion, by moving them from F1, F2, T3 and T4 to A1, F2, F7 and F8. The subject-dependent accuracy, using the improved locations, increased to 91.75 % from 86.83 % and 75 % of participants achieved a classification accuracy higher than 90 %, compared with only 16 % of participants before improving the electrode arrangement.

A Wearable In-Ear EEG Device for Emotion Monitoring

“…Scalp-based emotion classification by multi-channel EEG has been an active field of research [1,7,8,9,10,11,12,13,14,15,16]. A review of some of those works can be found in [7].…”

“…Other areas of focus for scalp-based EEG emotion classification include those in [15,27], which look to develop wearable headband solutions. However, for monitoring purposes, these designs may suffer in conditions such as a warm climate; it might be uncomfortable to wear headband for a long duration due to sweating.…”

Mapping and Timing the (Healthy) Emotional Brain: A Review

“…Examples of these rating scales are the Bivariate Evaluation and Ambivalent Measures (BEAM), described in [18] and the three-dimensional space proposed in [19], which distinguishes between tension arousal and energy arousal. Emotional map from [13], adapted with permission.…”

“…After the stimulus onset, Wei defines a time range of [0.5-4] s as the temporal space in which emotional signals appear [13]. This period has been divided by some authors into three sections: Early [400-1100] ms, Middle [1000-3000] ms, Late [3000-5000] ms [32].…”

Development of User-Friendly Wearable Electronic Textiles for Healthcare Applications

“…There are increasing levels of research activity focusing on integrating dry electrodes into textiles for wearable healthcare applications. However, most applications focus on diagnostics and monitoring such as ECG [ 14 , 15 ], EEG [ 16 , 17 ] and EMG [ 18 , 19 ]. Their application in therapeutics is limited mainly due to the issue of discomfort caused by the high impedance between the dry electrode and skin [ 20 , 21 ].…”