Looking for Emotions on a Single EEG Signal

Quesada-Tabares, Roylán; Molina, Alberto; Escudero-Fombuena, José I.; Merino-Monge, Manuel; Gómez, I.; Lebrato-Vázquez, Clara; Castro-García, Juan A.

doi:10.1007/978-3-030-27950-9_5

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Namely, stress produces an increase in the electrical activity of the sympathetic nervous system (SNS), that along with the inhibition of the parasympathetic nervous system (PSNS), makes changes in heart rate (HR), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST). Several papers have directly analyzed emotions through EEG [49][50][51][52][53] or studied their effects on variables such as HR [54,55], EDA [55,56], BR [57], or ST [56]. Combining several of these signals in a multimodal approach may benefit detection accuracy [58].…”

Section: Stress Detectionmentioning

confidence: 99%

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

et al. 2022

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Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.

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Section: Stress Detectionmentioning

confidence: 99%

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

et al. 2022

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“…We applied the algorithm published in [ 45 ] for the detection of single blinks. It is based on obtaining two main features from the raw EEG signal: (1) the difference between the maximum and minimum value in the epoch and (2) the energy of the ’blink-free’ EEG signal resulting from removing the baseline obtained by applying a Savitzky–Golay low-pass filter (order 2 and length 35) to the EEG epoch.…”

Section: Methodsmentioning

confidence: 99%

Controlling a Mouse Pointer with a Single-Channel EEG Sensor

Molina

Castro-García

Gómez-Bravo

et al. 2021

Sensors

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(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.

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Looking for Emotions on a Single EEG Signal

Cited by 2 publications

References 25 publications

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Controlling a Mouse Pointer with a Single-Channel EEG Sensor

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