Emotiv EPOC Neuroheadset Research Articles

Power Spectrum and Connectivity Analysis in EEG Recording during Attention and Creativity Performance in Children

The present research aims at examining the power spectrum and exploring functional brain connectivity/disconnectivity during concentration performance, as measured by the d2 test of attention and creativity as measured by the CREA test in typically developing children. To this end, we examined brain connectivity by using phase synchrony (i.e., phase locking index (PLI) over the EEG signals acquired by the Emotiv EPOC neuroheadset in 15 children aged 9- to 12-years. Besides, as a complement, a power spectrum analysis of the acquired signals was performed. Our results indicated that, during d2 Test performance there was an increase in global gamma phase synchronization and there was a global alpha and theta band desynchronization. Conversely, during CREA task, power spectrum analysis showed a significant increase in the delta, beta, theta, and gamma bands. Connectivity analysis revealed marked synchronization in theta, alpha, and gamma. These findings are consistent with other neuroscience research indicating that multiple brain mechanisms are indeed involved in creativity. In addition, these results have important implications for the assessment of attention functions and creativity in clinical and research settings, as well as for neurofeedback interventions in children with typical and atypical development.

Exploiting the Data Sensitivity of Neurometric Fidelity for Optimizing EEG Sensing

With newly developed wireless neuroheadsets, electroencephalography (EEG) neurometrics can be incorporated into in situ and ubiquitous physiological monitoring for human mental health. As a resource constraint system providing critical health services, the EEG headset design must consider both high application fidelity and energy efficiency. However, through empirical studies with an off-the-shelf Emotiv EPOC Neuroheadset, we uncover a mismatch between lossy EEG sensor communication and high neurometric application fidelity requirements. To tackle this problem, we study how to learn the sensitivity of neurometric application fidelity to EEG data. The learned sensitivity is used to develop two algorithms: 1) an energy minimization algorithm minimizing the energy usage in EEG sampling and networking while meeting applications’ fidelity requirements and 2) a fidelity maximization algorithm maximizing the sum of all applications’ fidelities through the incorporation and optimal utilization of a limited data buffer. The effectiveness of our proposed solutions is validated through trace-driven experiments.

Data Acquisition in Non-Invasive Brain-Computer Interface Using Emotiv Epoc Neuroheadset

This paper deals with the issue of the brain-computer interface (BCI) – the human-machine interface (HMI) based on acquisition, analysis and transformation of signals generated by the central nervous system (CNS) as the manifestation of its normal function. Brain-computer interface can be seen as the bridge that is building up direct one-way or two-way communication pathway between the brain and the external technical device. Paper introduces techniques based on non-invasive functional imaging of the brain used for data acquisition in non-invasive brain computer interfaces, and is focused on the technique that is reading neural activity of the brain with use of multi-channel electroencephalograph (EEG). As the part of this paper we are introducing our experience with the low-cost commercially available equipment Emotiv EPOC Neuroheadset based on this technology.

The Emotiv EPOC neuroheadset: an inexpensive method of controlling assistive technologies using facial expressions and thoughts?

PurposeThe EPOC neuroheadset is a commercially available device that allows game players to control a computer using their facial expressions or their thoughts. This paper aims to examine whether it has the potential to be used as an input for assistive technology (AT) devices.Design/methodology/approachTwo experiments were conducted. In the first, 12 non‐impaired subjects used the neuroheadset to control a computer with their facial expressions. They also used a simple system of two head switches for comparison. In the second experiment, three non‐impaired subjects were trained to use the neuroheadset to control a computer with their thoughts.FindingsIn the first experiment, the neuroheadset was slower and less accurate than the head switches (p<0.05), and was also harder to use. It is unlikely to be preferred to existing methods of accessing AT for those that retain a small amount of head movement. In the second experiment, by the end of the week, all three subjects achieved accuracy rates greater than chance.Research limitations/implicationsAll subjects were non‐impaired, and the sample size in the second experiment was small. Further research should concentrate on the second experiment, using larger sample sizes and impaired subjects.Practical implicationsThe EPOC neuroheadset is substantially cheaper than similar specialist devices, and has the potential to allow those with no voluntary muscle control to access AT with their thoughts.Originality/valueThe results of these two experiments show that the Emotiv EPOC neuroheadset can be used as an interface for non‐impaired users to transfer information to a computer, which could in turn be used to control AT.