Abstract

Multimodal bio-signals acquisition based on wearable devices and using virtual reality (VR) as stimulus source are promising techniques in emotion recognition research field. Numerous studies have shown that emotional states can be better evoked through Immersive Virtual Environments (IVE). The main goal of this paper is to provide researchers with a system for emotion recognition in VR environments. In this paper, we present a wearable forehead bio-signals acquisition pad which is attached to Head-Mounted Displays (HMD), termed HMD Bio Pad. This system can simultaneously record emotion-related two-channel electroencephalography (EEG), one-channel electrodermal activity (EDA), photoplethysmograph (PPG) and skin temperature (SKT) signals. In addition, we develop a human-computer interaction (HCI) interface which researchers can carry out emotion recognition research using VR HMD as stimulus presentation device. To evaluate the performance of the proposed system, we conducted different experiments to validate the multimodal bio-signals quality, respectively. To validate EEG signal, we have assessed the performance in terms of EEG eyes-blink task and eyes-open and eyes-closed task. The EEG eyes-blink task indicates that the proposed system can achieve comparable EEG signal quality in comparison to the dedicated bio-signals measuring device. The eyes-open and eyes-closed task proves that the proposed system can efficiently record alpha rhythm. Then we used signal-to-noise ratio (SNR) and Skin Conductance Reaction (SCR) signal to validate the performance for EDA acquisition system. A filtered EDA signal, with a high mean SNR of 28.52 dB, is plotted on HCI interface. Moreover, the SCR signal related to stimulus response can be correctly extracted from EDA signal. The SKT acquisition system has been validated effectively by the temperature change experiment when subjects are in unpleasant emotion. The pulse rate (PR) estimated from PPG signal achieved the low mean average absolute error (AAE), which is 1.12 beats per minute (BPM) over 8 recordings. In summary, the proposed HMD Bio Pad offers a portable, comfortable and easy-to-wear device for recording bio-signals. The proposed system could contribute to emotion recognition research in VR environments.

Highlights

  • The Head-Mounted Displays (HMD) Bio Pad is connected to the virtual reality (VR) HMD using a velcro fastener, and the multimodal bio-signals acquired from the forehead are transmitted to the human-computer interaction (HCI)

  • The consistency of EEG signal with eyes blink between HMD Bio Pad and Biopac is verified by eyes-blink task

  • The feasibility of electrodermal activity (EDA) acquisition system is proved from the signal-to-noise ratio (SNR) and the Skin Conductance Reaction (SCR) extracted from EDA signal after stimulation

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Summary

Introduction

All the above researches indicate that with the development of wearable technology, the effectiveness of the use for wearable devices to collect bio-signals for emotion recognition has proven. None of the wearable devices mentioned above can synchronously collect emotion-related EEG signals and other peripheral bio-signals. These devices lack a unified interface to perform stimulus selection, data acquisition, and emotion modeling simultaneously. A wearable forehead bio-signals acquisition device called HMD Bio Pad is developed which has the advantages of portability, comfort and ease of wearing; Using metal dry electrodes and attaching flexible sensors pad to HMD can greatly reduce experimental preparation time and improve the convenience of the system. Conclusions and future works are discussed in the last Section

System Overview
The Hardware Design of HMD Bio Pad
EEG Acquisition System
EDA Acquisition System
SKT Acquisition System
PPG Acquisition System
HCI Interface Software Design
Data Visualization Interface
Experimental Paradigm Setting Interface
Emotion Modeling Interface
System Structure Evaluation
EEG Eyes-Blink Task
Eyes-Open and Eyes-Closed Task
Signal-to-Noise Ratio
Skin Conductance Reaction
SKT Acquisition System Evaluation
PPG Acquisition System Evaluation
Software
Findings
Conclusions
Full Text
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