A smartphone interface for a wireless EEG headset with real-time 3D reconstruction. Affective Computing and Intelligent Interaction (ACII 2011),

We demonstrate a fully functional handheld brain scanner consisting of a low-cost 14-channel EEG headset with a wireless connection to a smartphone, enabling minimally invasive EEG monitoring in naturalistic settings. The smartphone provides a touch-based interface with real-time brain state decoding and 3D reconstruction

Smartphones get emotional: mind reading images and reconstructing the neural sources, 1st workshop on machine learning for affective computing (MLAC) at the Affective Computing and Intelligent Interaction (ACII 2011)

Combining a 14 channel neuroheadset with a smartphone to capture and process brain imaging data, we demonstrate the ability to distinguish among emotional responses reflected in different scalp potentials when viewing pleasant and unpleasant pictures compared to neutral content. Clustering independent components across subjects we are able to remove artifacts and identify common sources of synchronous brain activity, consistent with earlier findings based on conventional EEG equipment. Applying a Bayesian approach to reconstruct the neural sources not only facilitates differentiation of emotional responses but may also provide an intuitive interface for interacting with a 3D rendered model of...

Emotiv Experimenter, An experimentation and mind-reading application for the Emotiv EPOC, Princeton University 2011

This report describes the development and features of Experimenter, an application based on the EEG capabilities of the Emotiv EPOC headset. As a research tool, Experimenter allows a variety of experiments based on classic stimulus-presentation paradigms to be run using the Emotiv. Unlike most EEG setups, however, Experimenter not only records data but also attempts online analysis and classification of the incoming data stream. With the proper experimental setup, then, Experimenter can be used as a simple mind-reading application. Experiment and application design, sample procedures, classification techniques, results, and technical details are discussed.

P-300 Rhythm Detection Using ANFIS Algorithm and Wavelet Feature Extraction in EEG Signals, Proceedings of the World Congress on Engineering and Computer Science Vol 1, 963-968, 2010

P300 evoked potential is an electroencephalographic (EEG) signal obtained at the central-parietal region of the brain in response to rare or unexpected events. In this work, an experiment on the detection of a P-300 rhythm for potential applications on brain computer interfaces (BCI) using an Adaptive Neuro Fuzzy algorithm (ANFIS) is presented. The P300 evoked potential is obtained from visual stimuli followed by a motor response from the subject. The EEG signals are obtained with a 14 electrodes Emotiv EPOC headset. Preprocessing of the signals includes denoising and blind source separation using an Independent Component Analysis algorithm. The P300 rhythm is detected...