Anibal Cotrina

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.

Adaptive BCI based on software agents

The selection of features is generally the most difficult field to model in BCIs. Therefore, time and effort are invested in individual feature selection prior to data set training. Another great difficulty regarding the model of the BCI topology is the brain signal variability between users. How should this topology be in order to implement a system that can be used by large number of users with an optimal set of features? The proposal presented in this paper allows for obtaining feature reduction and classifier selection based on software agents. The software agents contain Genetic Algorithms (GA) and a cost function. GA used entropy and mutual information to choose the number of features. For the classifier selection a cost function was defined. Success rate and Cohens Kappa coefficient are used as parameters to evaluate the classifiers performance. The obtained results allow finding a topology represented as a neural model for an adaptive BCI, where the number of the channels, features and the classifier are interrelated. The minimal subset of features and the optimal classifier were obtained with the adaptive BCI. Only three EEG channels were needed to obtain a success rate of 93% for the BCI competition III data set IVa.

A SSVEP-BCI Setup Based on Depth-of-Field

In optical systems, the range of distance near the point of focus where objects are perceived sharply is referred as depth-of-field; objects outside this region are defocused and blurred. Furthermore, ophthalmology studies state that the amplitude and the latency of visual evoked potentials are affected by defocusing. In this context, this paper evaluates a novel setup for a steady-state visual evoked potential (SSVEP) brain–computer interface, in which two stimuli are presented together in the center of the user’s field of view but at different distances ensuring that if one stimulus is focused on, the other one is non-focused, and vice versa. The evaluationwas conductedwith eight healthy subjects who were asked to focus on just one stimulus at a time. An average accuracy rate of 0.93 was achieved for a time window of 4 s by employing well know SSVEP detection methods. Results show that distinguishable SSVEP can be elicited by the focused stimulus regardless of the non-focused one is also present in the field of view. Finally, this approach allows users to send commands through a stimuli selection by focusing mechanism that does not demand neck, head, and/or eyeball movements.

Comparison among 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 presents the lowest relation value. This technique is referred to Ratio-PSDA. The third and final techniques 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, the Information Transfer Rate (ITR) and the computational cost for several windows length of each stimulus frequency. The results showed that the Hybrid-PSDA-CCA method achieved the best result with an average accuracy of 91.14%.

Future Directions in Patients with Paralysis

This book describes a novel approach of human-computer interaction based on SSVEP-BCI and Depth-of-field.

Frequency Demodulation for a SSVEP-BCI Based on Depth-of-Field

In traditional SSVEP-BCI systems, the analysis and the signal processing usually are performed considering only the response of the gazed stimulus that is placed on the center of the field.

Fundamentals: From Light to Command

In this chapter, a theoretical background is provided. It starts by defining the light and ends by presenting a BCI command due to the light stimulus. Figure 2.1 illustrates the “path of the light” that is emitted by a visual stimulus and sensed by the human visual system [1].

Offline Evaluation of Command Detection

In Chap. 4, the spatial-temporal spectral response caused by a focused stimulus was studied, even if a non-focused stimulus is also present in the field of view.

The Novel Stimulation Setup

Depth-of-field of the human eyes is the range of distances near the point of focus where the eyes perceive the image sharply.

Online Applications in Communication and Control

Currently, BCIs are developing alternatives ways for augmentative communication or control technology for patients with severe neuromuscular disorders (Wolpaw et al. (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113(6):767–91, [1]). The aim of this kind of technology is to improve the patients quality of life that can allow them more independence.