Bacteria Hunt: A multimodal, multiparadigm BCI game

Brain-Computer Interfaces (BCIs) allow users to control applications by brain activity. Among their possible applications for non-disabled people, games are promising candidates. BCIs can enrich game play by the mental and affective state information they contain. During the eNTERFACE’09 workshop we developed the Bacteria Hunt game which can be played by keyboard and BCI, using SSVEP and relative alpha power. We conducted experiments in order to investigate what difference positive vs. negative neurofeedback would have on subjects’ relaxation states and how well the different BCI paradigms can be used together. We observed no significant difference in mean alpha band power, thus relaxation, and in user experience between the games applying positive and negative feedback. We also found that alpha power before SSVEP stimulation was significantly higher than alpha power during SSVEP stimulation indicating that there is some interference between the two BCI paradigms

Bacteria Hunt: Evaluating multi-paradigm BCI interaction

The multimodal, multi-paradigm brain-computer interfacing (BCI) game Bacteria Hunt was used to evaluate two aspects of BCI interaction in a gaming context. One goal was to examine the effect of feedback on the ability of the user to manipulate his mental state of relaxation. This was done by having one condition in which the subject played the game with real feedback, and another with sham feedback. The feedback did not seem to affect the game experience (such as sense of control and tension) or the objective indicators of relaxation, alpha activity and heart rate. The results are discussed with regard to clinical neurofeedback studies. The second goal was to look into possible interactions between the two BCI paradigms used in the game: steady-state visually-evoked potentials (SSVEP) as an indicator of concentration, and alpha activity as a measure of relaxation. SSVEP stimulation activates the cortex and can thus block the alpha rhythm. Despite this effect, subjects were able to keep their alpha power up, in compliance with the instructed relaxation task. In addition to the main goals, a new SSVEP detection algorithm was developed and evaluated

SSVEP-Based BCIs

This chapter describes the method of flickering targets, eliciting fundamental frequency changes in the EEG signal of the subject, used to drive machine commands after interpretation of user’s intentions. The steady-state response of the changes in the EEG caused by events such as visual stimulus applied to the subject via a computer screen is called steady-state visually evoked potential (SSVEP). This feature of the EEG signal can be used to form a basis of input to assistive devices for locked in patients to improve their quality of life, as well as for performance enhancing devices for healthy subjects. The contents of this chapter describe the SSVEP stimuli; feature extraction techniques, feature classification techniques and a few applications based on SSVEP based BCI

BNCI systems as a potential assistive technology: ethical issues and participatory research in the BrainAble project

This paper highlights aspects related to current research and thinking about ethical issues in relation to Brain Computer Interface (BCI) and Brain-Neuronal Computer Interfaces (BNCI) research through the experience of one particular project, BrainAble, which is exploring and developing the potential of these technologies to enable people with complex disabilities to control computers. It describes how ethical practice has been developed both within the multidisciplinary research team and with participants. Results: The paper presents findings in which participants shared their views of the project prototypes, of the potential of BCI/BNCI systems as an assistive technology, and of their other possible applications. This draws attention to the importance of ethical practice in projects where high expectations of technologies, and representations of “ideal types” of disabled users may reinforce stereotypes or drown out participant “voices”. Conclusions: Ethical frameworks for research and development in emergent areas such as BCI/BNCI systems should be based on broad notions of a “duty of care” while being sufficiently flexible that researchers can adapt project procedures according to participant needs. They need to be frequently revisited, not only in the light of experience, but also to ensure they reflect new research findings and ever more complex and powerful technologies

A ML-based Approach to Enhance Metrological Performance of Wearable Brain-Computer Interfaces

In this paper, the adoption of Machine Learning (ML) classifiers is addressed to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces (BCIs). The proposed BCI is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In this setup, Augmented Reality Smart Glasses are used to generate and display the flickering stimuli for the SSVEP elicitation. An experimental campaign was conducted on 20 adult volunteers. Successively, a Leave-One-Subject-Out Cross Validation was performed to validate the proposed algorithm. The obtained experimental results demonstrate that suitable ML-based processing strategies outperform the state-of-the-art techniques in terms of classification accuracy. Furthermore, it was also shown that the adoption of an inter-subjective model successfully led to a decrease in the 3-σ uncertainty: this can facilitate future developments of ready-to-use systems

EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs

Enhancement of SSVEPs Classification in BCI-based Wearable Instrumentation Through Machine Learning Techniques

This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies provide a significant enhancement of the SSVEP classification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1-σ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems

A portable EEG-BCI framework enhanced by machine learning techniques

Brain Computer Interfaces (BCIs) allow direct communication between the human brain and external devices through the processing and interpretation of brain signals. Indeed, BCI represents the ultimate achievement in human-machine interaction, eliminating all the intermediate physical steps between the intention of an action and its implementation. Electroencephalography (EEG) plays a key role in BCIs being the least invasive technique for capturing brain electrical activity. However, high performance devices turn out to be uncomfortable and of unpractical use outside dedicated facilities, mainly due to the use of many electrodes. Conversely, single-channel EEG devices made of fewer electrodes provide weak and noisy signals difficult to interpret. In this PhD thesis, a portable BCI prototype enhanced by machine learning techniques for the classification of brain signals — and in particular of Steady State Visual Evoked Potentials (SSVEPs) — is proposed. The current study embraces the design, realization, characterization, and optimization of a BCI built on top of a cost-effective single-channel EEG device. The results have been validated both in offline and online sessions thanks to the collaboration of volunteers equipped with the given prototype. Due to its usability, the proposed framework may broaden the range of state-of-the-art BCI applications

Adoption of Machine Learning Techniques to Enhance Classification Performance in Reactive Brain-Computer Interfaces

This paper proposes the adoption of an innovative algorithm to enhance the performance of highly wearable, reactive Brain-Computer Interfaces (BCIs), which exploit the Steady-State Visually Evoked Potential (SSVEP) paradigm. In particular, a combined time-domain/frequency-domain processing is performed in order to reduce the number of features of the brain signals acquired. Successively, these features are classified by means of an Artificial Neural Network (ANN) with a learnable activation function. In this way, the user intention can be translated into commands for external devices. The proposed algorithm was initially tested on a benchmark data set, composed by 35 subjects and 40 simultaneous flickering stimuli, obtaining performance comparable with the state of the art. Successively, the algorithm was also applied to a data set realized with highly wearable BCI equipment. In particular, (i) Augmented Reality (AR) smart glasses were used to generate the flickering stimuli necessary to the SSVEPs elicitation, and (ii) a single-channel EEG acquisition was conducted for each volunteer. The obtained results showed that the proposed strategy provides a significant enhancement in SSVEPs classification with respect to other state-of-the-art algorithms. This can contribute to improve reliability and usability of brain computer interfaces, thus favoring the adoption of this technology also in daily-life applications