Emotions Detection based on a Single-electrode EEG Device

Tabares, Roylán Quesada; Molina, Alberto; González, Isabel María Gómez; Monge, Manuel Merino; Castro-García, Juan A.; Cabrera, Rafael Cabrera

doi:10.5220/0006476300890095

Cited by 13 publications

(16 citation statements)

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“…In addition to EEG, triaxial accelerometer data is automatically collected. You can also use a microSD card to save data offline in Holter mode; and as like as Shimmer device, it can use Bluetooth to transmit real time data too [28].…”

Section: Acquisition Devicesmentioning

confidence: 99%

“…If we compare all tests (from test , it is possible to note that again, the surprised emotion kept with best recognition values (low errors), as shown in Figures 9 and 10, which it present all considered errors along the tests. The higher recognition errors were reached when the EEG datasets were omitted in different tests (tests 15-18 and tests [31][32][33][34], showing that in these tests, the recognition results were better when all data were considered; when GSR datasets were ommited, the results got good predictions too (tests 11-14 and tests [27][28][29][30]. The application of feature selection based on SVD and the omission of GSR datasets, returned the less recogmition errors (tests [27][28][29][30].…”

Section: Dsmentioning

confidence: 99%

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Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

Roza

Postolache

2019

Sensors

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The present work tries to fill part of the gap regarding the pilots' emotions and their bio-reactions during some flight procedures such as, takeoff, climbing, cruising, descent, initial approach, final approach and landing. A sensing architecture and a set of experiments were developed, associating it to several simulated flights (N f lights = 13) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator (N pilots = 8). It is shown that it is possible to recognize emotions from different pilots in flight, combining their present and previous emotions. The cardiac system based on Heart Rate (HR), Galvanic Skin Response (GSR) and Electroencephalography (EEG), were used to extract emotions, as well as the intensities of emotions detected from the pilot face. We also considered five main emotions: happy, sad, angry, surprise and scared. The emotion recognition is based on Artificial Neural Networks and Deep Learning techniques. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were the main methods used to measure the quality of the regression output models. The tests of the produced output models showed that the lowest recognition errors were reached when all data were considered or when the GSR datasets were omitted from the model training. It also showed that the emotion surprised was the easiest to recognize, having a mean RMSE of 0.13 and mean MAE of 0.01; while the emotion sad was the hardest to recognize, having a mean RMSE of 0.82 and mean MAE of 0.08. When we considered only the higher emotion intensities by time, the most matches accuracies were between 55% and 100%.

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Section: Acquisition Devicesmentioning

confidence: 99%

Section: Dsmentioning

confidence: 99%

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

Roza

Postolache

2019

Sensors

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“…Data and events from sensors and the application have to be synchronized to delimit the period of time associated to each highlighted ideogram. A similar situation is described in [37] where authors searched for identified emotional states, through EEG signals, with subjects having to watch affective pictures for several seconds [41]. Every segment of collected data also had to be associated to each projected image.…”

Section: Introductionmentioning

confidence: 99%

An Easy-to-Use Multi-Source Recording and Synchronization Software for Experimental Trials

et al. 2020

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A typical routine in many scientific studies consists of recording data from devices and identifying which segment of data corresponds with an experimental interval. However, many current applications have been designed to obtain and save data from one single device, and synchronizing data with the markers that delimit the test phases can be difficult. To address this issue, we have developed LSRec, which is based on Lab-Streaming Layer, a C++ library that allows data synchronization. LSRec is an easy-to-use, open-source, multi-platform, recording system developed on Java that can save data from several devices at the same time, while maintaining synchronization with the experimental phase markers. It supports three explicit sync methods: the first uses one-integer-channel input Lab-Streaming Layer streams. The others use TCP/UDP socket messages and allow any existing software that generates sync markers through TCP/UDP messages to be used under the same conditions. In LSRec, the markers are saved together with input data, facilitating the process of linking data with test stages. A prerequisite for recording software is to guarantee suitable timing performance, with no data loss and an easy user interface. These features were assessed for LSRec, with no data loss detected for 20 hours (25 sessions). We evaluated performance by measuring timestamp deviations for sync marker and input data. The sync methods exhibited an average of deviations of around −34µs (which is more than acceptable, e.g., in studies involving living beings), whereas the absolute value of deviation for one-channel data was lower than 40µs. Finally, we assessed the system's usability with the technology acceptance model 3 survey (6 volunteers). Subjects saw the software as useful, and intended to use it in the future. In conclusion, LSRec is a useful tool for those who need to record data from multiple sources, and maintain synchronization with experimental phases with low delay.

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“…The first is the cheapest, with one electrode, followed by Muse, with 4 channels and, finally, Emotiv with up to 14 channels. Previous works have shown the efficacy of using NM as computer access method, by modulating the level of users' attention [54], or for detecting a reduced number of emotions [71]. In [13], NM and Emotiv were compared to detect cognitive loads.…”

Section: Introductionmentioning

confidence: 99%

Communication Technologies Based on Voluntary Blinks: Assessment and Design

et al. 2019

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Some people with severe disabilities are confined in a state in which communication is virtually impossible, being reduced to communicating with their eyes or using sophisticated systems that translate thoughts into words. The EyeTrackers and Brain-Computer Interfaces (BCIs) are suitable systems for those people but their main drawback is their cost. More affordable devices are capable of detecting voluntary blinks and translating them into a binary signal that allows the selection, for example, of an ideogram on a communication board. We tested four different systems based on infrared, bioelectrical signals (Electro-Oculography (EOG) and Electro-Encephalography (EEG)), and video processing. The experiments were performed by people with/without disabilities and analyzed the systems' performances, usability, and method of voluntary blinking (long blinks or sequence of two short blinks). The best accuracy (99.3%) was obtained using Infrared-Oculography (IR-OG) and the worst with the EEG headset (85.9%) and there was a statistical influence of the technology on accuracy. Regarding the method of voluntary blinking, the use of long or double blinks had no statistical influence on accuracy, excluding EOG, and the time taken to perform double blinks was shorter, resulting in a potentially much faster interface. People with disabilities obtained similar values but with greater variability. The preferred technology and blinking methods were Video-Oculography (VOG) and long blinks, respectively. The several Open-Source Hardware (OSHW) devices have been developed and a new algorithm for detecting voluntary blinks has also been proposed, which outperforms most of the published papers in the reviewed literature.

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Emotions Detection based on a Single-electrode EEG Device

Cited by 13 publications

References 0 publications

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

An Easy-to-Use Multi-Source Recording and Synchronization Software for Experimental Trials

Communication Technologies Based on Voluntary Blinks: Assessment and Design

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