Javier Castillo

Towards an architecture of a hybrid BCI based on SSVEP-BCI and passive-BCI.

Recent decades have seen BCI applications as a novel and promising new channel of communication, control and entertainment for disabled and healthy people. However, BCI technology can be prone to errors due to the basic emotional state of the user: the performance of reactive and active BCIs decrease when user becomes stressed or bored, for example. Passive-BCI is a recent approach that fuses BCI technology with cognitive monitoring, providing valuable information about the users intentions, the situational interpretations and mainly the emotional state. In this work, an architecture composed by passive-BCI co-working with SSVEP-BCI is proposed, with the aim of improving the performance of the reactive-BCI. The possibility of adjusting recognition characteristics of SSVEP-BCIs using a passive-BCI output is evaluated. In this sense, two ways to recover the accuracy of SSVEP are presented in this paper: 1) Adjusting of Amplitude of the SSVEP and 2) Adjusting of Frequency of the SSVEP response. The results are promising, because accuracy of SSVEP-BCI can be recovered in the case that it was reduced by the BCI users emotional state.

Mobile robotics: A tool for interaction with children with autism

This paper presents an implementation with mobile robot to generate actions to interact with children with autism. The robot is able to detect the child localization and approach him/her, keeping a safe proximity minimum (interaction distance). The mobile robot is equipped with laser sensor to obtain distances and an embedded control system for the interaction. The implementation allows two modes of interaction, depending on the degree of interaction with the child. The system here developed can help children with autism in the process of social evolution and be a tool for professionals and researchers of the area.

An Ethernet sniffer for On-line acquisition of EEG with the BrainNet36® device applied to a BCI

This paper presents an Ethernet sniffer programmed in ANSI C to export in an on-line way the electroencephalographic (EEG) data acquired with the device BrainNet36®, which is a Brazilian EEG device for clinical and polysomnography purposes. The sniffer proved to be useful for off-line analysis of the EEG and also for on-line applications, as Brain Computer-Interfaces (BCIs). Despite its limitations and packet losses at around 1.7% due to noise, the off-line analysis of the EEG successfully replicated results of the literature regarding the Event-Related (De)Synchronization (ERD/ERS) and Evoked Potentials (EPs) calculated for mental tasks. For on-line applications the sniffer was used to program a single-switch BCI for on-line classification of motor and no motor mental tasks with high success rate. Nowadays, the BrainNet36® device is being used for EEG research at many Brazilian universities, therefore, we hope that this article may encourage on-line applications. Finally, as the sniffer operation is explained here with examples, this text may serve as a reference guide for potential users.

Proposal of a Brain Computer Interface to command an autonomous car

This paper presents a proposal of Brain Computer Interface (BCI) to command an autonomous car. This BCI is based on the paradigm of visual evoked potentials (VEP) and event-related desynchronization (ERD). A menu interface is presented to the user with disabilities in order he/she can choose a destination for the autonomous car. The selection of the final destination is performed using visual stimuli flickering at different frequencies, analyzed through the analysis of brain signals present at the occipital region of the users scalp. The power spectrum of these signals is then obtained, in order to get the frequency of the visual stimulation. Preliminary tests were performed with healthy users and people with disabilities, which reached an average success rate above 90%. The proposed system is also capable of turning on/off the stimuli, thus reducing the fatigue associated with particular visual stimuli.

Using Brain-Computer Interface to control an avatar in a Virtual Reality Environment

The proposal of this research is to present the development of a tool that might be useful in rehabilitation, for subjects with disability, that suffer from some kind of limbs movement limitation. This tool carries a 3D Virtual Reality Environment (VRE), which emulates the movement of a healthy person, using the immersion of the subject through an avatar. To do so, and test its feasibility, pre acquired motor imagery signals were used to test the VRE as an off-line Brain Computer Interface (BCI) feedback. The subjects brain waves were captured by an Electroencephalography (EEG) equipment. For training the classifier, 45 trials, 25 seconds long, were used, and 15 trials for its validation. Five mental tasks were tested with the BCI, and the one with the best results (imagination of the manipulation of a cube) was used to move the avatar through a virtual room.

Feature extraction techniques based on power spectrum for a SSVEP-BCI

This paper presents a comparison among three methods for Steady-State Visually Evoked Potentials (SSVEP) detection. These techniques are based on Power Spectral Density Analysis (PSDA) and Canonical Correlation Analysis (CCA). The first method estimates the signal-to-noise ratio of the power spectrum in each stimulus frequency using PSDA, which is called Traditional-PSDA. The second analysis estimates the relation between the difference of the stimulus frequency and its neighbor frequencies, using the power spectrum in these neighbor frequencies, and seeks the neighbor frequency which present the lowest relation value. This technique is referred to Ratio-PSDA. The third and final technique called Hybrid-PSDA-CCA. The performances of the methods were evaluated using a database of electroencephalogram (EEG) signals. The EEG signals were recorded from 19 volunteers, from which six people present disabilities. They were stimulated with visual stimuli flickering at 5.6, 6.4, 6.9 and 8.0 Hz. The system performance was evaluated considering the accuracy and the Information Transfer Rate (ITR) for each stimulus frequency. The results showed that the Hybrid-PSDA-CCA method achieved the best result with an average accuracy of 91.44%.

EEG analysis and mobile robot as tools for emotion characterization in autism.

Background Autism or “Autism Spectrum Disorder” is characterized by manifestations of impairments in social behavior, stereotyped movements, difficulty in communication and interaction with people. This paper presents a system composed of a mobile robot to generate interactive tasks with autistic children, and an EEG (electroencephalography) equipment to get brain information to characterize their emotions.

Brain-Computer Interface (BCI) combined with Virtual Reality Environment (VRE) for inferior limbs rehabilitation in post-stroke subjects

More than 10 million people in the world live with some kind of motion handicap caused by a Central Nervous System (CNS) dysfunction. Stroke is the major cause of this disability in the adult population. Due to the increase of elderlies in the worlds population, such index tends to increase. The proposal of this research is to provide a tool for rehabilitation, useful for subjects that suffer from lower limbs movement handicap, acquired by stroke. This tool carries a 3D Virtual Reality Environment (VRE), which emulates the movement of a heath person, using the immersion of the subject through an avatar. The subject´s brain generates commands to control the avatar while conducting the rehabilitation process. The brain waves are captured by an Electroencephalography (EEG) equipment, that information is sent to a computer for processing, which sends the information to the virtual environment to control the avatar, completing, thereby, the Brain-Computer Interface (BCI) tool. This system asks two different tasks for the subject: move the left or right leg, stimulating brain´s areas responsible for each one of those motor activities, implying thereby, in the rehabilitation process. The VRE provides, for the subject, a feedback of his/her motion intentions. The system works as an attractive environment, which motivates the subject to use it, and, at the same time, is useful to evaluate his/her treatment evolution.