Pilot Study of Emotion Recognition through Facial Expression

This paper presents our finding from a pilot study on human reaction through facial expression as well as brainwave changes when being induced by audio-visual stimuli while using the Emotiv Epoc equipment. We hypothesize that Emotiv Epoc capable to detect the emotion of the participants and the graphs would match with facial expression display. In this study, four healthy men were chosen and being induced with eight videos, six videos are predefined whereas the other two videos are personalized. We aim for identifying the optimum set up for the real experiment, to validate the capability of the Emotiv Epoc and to obtain spontaneous facial expression database. Thus, from the pilot study, the principal result shows that emotion is better if being induced by using personalized videos. Not only that, it also shows the brainwave produced by Emotiv Epoc is aligned with the facial expression especially for positive emotion cases. Hence, it is possible to obtain spontaneous database in the present of Emotiv Epoc

A new method to detect event-related potentials based on Pearson\u2019s correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience. Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise. The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP\u2019s waveform, these waveforms being time- and phase-locked. In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson\u2019s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase\u2014in consonance with the stimuli\u2014in EEG signal correlation over all channels. This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs. These hidden components seem to be caused by variations (between each successive stimulus) of the ERP\u2019s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology. The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language

Interactive Reading Using Low Cost Brain Computer Interfaces

This work shows the feasibility for document reader user applications using a consumer grade non-invasive BCI headset. Although Brain Computer Interface (BCI) type devices are beginning to aim at the consumer level, the level at which they can actually detect brain activity is limited. There is however progress achieved in allowing for interaction between a human and a computer when this interaction is limited to around 2 actions. We employed the Emotiv Epoc, a low-priced BCI headset, to design and build a proof-of-concept document reader system that allows users to navigate the document using this low cast BCI device. Our prototype has been implemented and evaluated with 12 participants who were trained to navigate documents using signals acquired by Emotive Epoc

Implementation of Robotic arm control with Emotiv Epoc

Brain Computer Interface (BCI) has opened up a new hope for people suffering from severe motor disabilities, having no physical activities caused due to disease or injury to the central or peripheral nervous system. A BCI based robotic arm movement control is designed and implemented. The proposed system acquires data from the scalp of subjects a group of sensors. Emotiv EPOC a commercially available EEG headset is used, which analyzes the acquired EEG signals real time. The signals are processed and accordingly commands are issued for different movements which will be based on the characteristic patterns for various facial expressions, human emotions and cognitive actions. The idea is to combine the user intent with a robotic arm to achieve the user initiated motor movements

Control of a mobile robot through brain computer interface

This paper poses a control interface to com-mand the movement of a mobile robot according to sig-nals captured from the user’s brain. These signals are acquired and interpreted by Emotiv EPOC device, a 14-electrode type sensor which captures electroenceph-alographic (EEG) signals with high resolution, which, in turn, are sent to a computer for processing. One brain-computer interface (BCI) was developed based on the Emotiv software and SDK in order to command the mobile robot from a distance. Functionality tests are performed with the sensor to discriminate shift inten-tions of a user group, as well as with a fuzzy controller to hold the direction in case of concentration loss. As con-clusion, it was possible to obtain an efficient system for robot movementsEn este artículo se presenta una interfaz de control que permite comandar el movimiento de un robot móvil en función de la captura de señales provenientes del cerebro del usuario. Dichas señales son adquiridas e in-terpretadas por medio del dispositivo Emotiv Epoc, el cual cuenta con 14 sensores tipo electrodo que captan señales electroencefalográficas (EEG) de alta resolución, que des-pués son enviadas a un equipo de cómputo para ser pro-cesadas. Se desarrolla una interfaz cerebro-computador (BCI) basada en el software y SDK del desarrollador del Emotiv mediante la cual se comanda de forma remota el robot móvil. Se realizan pruebas de funcionalidad con el sensor para discriminar una intención de desplazamiento por parte de un grupo de usuarios y un controlador difuso para sostener la dirección en casos de perdida de la con-centración. Como conclusión, se logra obtener un sistema eficiente para la manipulación del robo

A video game design based on Emotiv Neuroheadset

This paper presents our work on the development of a video maze game in Android system, and a new method to play the game using gyroscope and electromyography (EMG) signals obtained by a wireless Emotiv Neuroheadset. The TeamViewer software is used to share the computer screen and to transfer the data to an Android device, and the Emotiv EPOC headset is used to detect the intension of the user who is playing the game. The cursor position is controlled using information from the gyroscope embeded in the headset. The clicks are generated through the users blinking action based on the expressive suite data acquired from Emotiv headset signal data. A program called Neuro Mousecontrol is used to act as a tool for controlling gyroscope movements and clicking actions together. Extensive tests have demonstrated the effectiveness of the developed system