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

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Wearable Brain-Computer Interface Instrumentation for Robot-Based Rehabilitation by Augmented Reality

An instrument for remote control of the robot by wearable brain-computer interface (BCI) is proposed for rehabilitating children with attention-deficit/hyperactivity disorder (ADHD). Augmented reality (AR) glasses generate flickering stimuli, and a single-channel electroencephalographic BCI detects the elicited steady-state visual evoked potentials (SSVEPs). This allows benefiting from the SSVEP robustness by leaving available the view of robot movements. Together with the lack of training, a single channel maximizes the device's wearability, fundamental for the acceptance by ADHD children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on ten healthy adult subjects highlighted an average accuracy higher than 83%, with information transfer rate (ITR) up to 39 b/min. Preliminary further tests on four ADHD patients between six- and eight-years old provided highly positive feedback on device acceptance and attentional performance

Classification of EEG signals of user states in gaming using machine learning

In this research, brain activity of user states was analyzed using machine learning algorithms. When a user interacts with a computer-based system including playing computer games like Tetris, he or she may experience user states such as boredom, flow, and anxiety. The purpose of this research is to apply machine learning models to Electroencephalogram (EEG) signals of three user states -- boredom, flow and anxiety -- to identify and classify the EEG correlates for these user states. We focus on three research questions: (i) How well do machine learning models like support vector machine, random forests, multinomial logistic regression, and k-nearest neighbor classify the three user states -- Boredom, Flow, and Anxiety? (ii) Can we distinguish the flow state from other user states using machine learning models? (iii) What are the essential components of EEG signals for classifying the three user states? To extract the critical components of EEG signals, a feature selection method known as minimum redundancy and maximum relevance method was implemented. An average accuracy of 85 % is achieved for classifying the three user states by using the support vector machine classifier --Abstract, page iii

Mobile advertising effectiveness versus PC and TV using consumer neuroscience

This Doctoral Thesis, entitled Mobile Advertising Effectiveness versus PC and TV, Using Consumer Neuroscience, while analyzes both the evolution of mobile advertising and its current situation, also discusses, how effective is mobile advertising when compared against advertising in other digital devices, such as PC and TV. The last few years have been characterized by an increase of the time that consumers spend on their mobile phones and as a result, by an increase in the expending on digital mobile advertising. Brands are already demanding models that measure digital advertising effectiveness, and consumer neuroscience technology may help, not only to measure it, but also to understand its impact on consumers. Considering this environment, this research proposes various recommendations for advertisers that may be considering using consumer neuroscience technology to measure mobile advertising effectiveness, as well as recommendations on how to design mobile ads that increase advertising effectiveness

MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware

Neurophysiological studies are typically conducted in laboratories with limited ecological validity, scalability, and generalizability of findings. This is a significant challenge for the development of brain-computer interfaces (BCIs), which ultimately need to function in unsupervised settings on consumer-grade hardware. We introduce MYND: A framework that couples consumer-grade recording hardware with an easy-to-use application for the unsupervised evaluation of BCI control strategies. Subjects are guided through experiment selection, hardware fitting, recording, and data upload in order to self-administer multi-day studies that include neurophysiological recordings and questionnaires. As a use case, we evaluate two BCI control strategies ("Positive memories" and "Music imagery") in a realistic scenario by combining MYND with a four-channel electroencephalogram (EEG). Thirty subjects recorded 70.4 hours of EEG data with the system at home. The median headset fitting time was 25.9 seconds, and a median signal quality of 90.2% was retained during recordings.Neural activity in both control strategies could be decoded with an average offline accuracy of 68.5% and 64.0% across all days. The repeated unsupervised execution of the same strategy affected performance, which could be tackled by implementing feedback to let subjects switch between strategies or devise new strategies with the platform.Comment: 9 pages, 5 figures. Submitted to PNAS. Minor revisio

Changes in corticospinal drive to spinal motoneurones following tablet-based practice of manual dexterity

The use of touch screens, which require a high level of manual dexterity, has exploded since the development of smartphone and tablet technology. Manual dexterity relies on effective corticospinal control of finger muscles, and we therefore hypothesized that corticospinal drive to finger muscles can be optimized by tablet‐based motor practice. To investigate this, sixteen able‐bodied females practiced a tablet‐based game (3 × 10 min) with their nondominant hand requiring incrementally fast and precise pinching movements involving the thumb and index fingers. The study was designed as a semirandomized crossover study where the participants attended one practice‐ and one control session. Before and after each session electrophysiological recordings were obtained during three blocks of 50 precision pinch movements in a standardized setup resembling the practiced task. Data recorded during movements included electroencephalographic (EEG) activity from primary motor cortex and electromyographic (EMG) activity from first dorsal interosseous (FDI) and abductor pollicis brevis (APB) muscles. Changes in the corticospinal drive were evaluated from coupling in the frequency domain (coherence) between EEG–EMG and EMG–EMG activity. Following motor practice performance improved significantly and a significant increase in EEG‐EMG(APB) and EMG(APB)‐EMG(FDI) coherence in the beta band (15–30 Hz) was observed. No changes were observed after the control session. Our results show that tablet‐based motor practice is associated with changes in the common corticospinal drive to spinal motoneurons involved in manual dexterity. Tablet‐based motor practice may be a motivating training tool for stroke patients who struggle with loss of dexterity

Low-Cost Assessment of User eXperience Through EEG Signals

EEG signals are an important tool for monitoring the brain activity of a person, but equipment, expertise and infrastructure are required. EEG technologies are generally expensive, thus few people are normally able to use them. However, some low-cost technologies are now available. One of these is OPENBCI, but it seems that it is yet to be widely employed in Human-Computer Interaction. In this study, we used OPENBCI technology to capture EEG signals linked to brain activity in ten subjects as they interacted with two video games: Candy Crush and Geometry Dash. The experiment aimed to capture the signals while the players interacted with the video games in several situations. The results show differences due to the absence/presence of sound; players appear to be more relaxed without sound. In addition, consistent analysis of the EEG data, meCue 2.0 and SAM data showed high consistency. The evidence demonstrates that interesting results are able to be gathered based on low-cost EEG (standard) signal-based technologies