Online home appliance control using EEG-Based brain-computer interfaces

Brain???computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 andN200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ?? 17.9%, the digital door-lock with 78.7% ?? 16.2% accuracy, and the light with 80.0% ?? 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs

In Pursuit of an Easy to Use Brain Computer Interface for Domestic Use in a Population with Brain Injury

This paper presents original research investigating a sensor based, ambient assisted smart home platform, within the framework of a brain computer interface (BackHome). This multimodal system integrates home-based sensors, mobile monitoring, with communication tools, web browsing, smart home control and cognitive rehabilitation. The target population are people living at home with acquired brain injury. This research engaged with the target population and those without brain injury, who provided a control for system testing. Aligned with our ethical governance a strong user centric ethos was foundational to participant engagement. Participant experience included three individual sessions to complete a pre-set protocol with supervision. Evaluation methodology included observations, time logging, completion of protocol and usability questionnaires. Results confirmed the average accuracy score for the people without brain injury was 82.6% (±4.7), performing best with the cognitive rehabilitation. Target end users recorded an average accuracy score of 76% (±11.5) with the speller logging the highest accuracy score. Additional outcomes included the need to refine the aesthetic appearance, as well as improving the reliability and responsiveness of the BCI. The findings outline the importance of engaging with end users to design and develop marketable BCI products for use in a domestic environment. DOI: 10.17762/ijritcc2321-8169.150610

Rapid P300 brain-computer interface communication with a head-mounted display

Visual ERP (P300) based brain-computer interfaces (BCIs) allow for fast and reliable spelling and are intended as a muscle-independent communication channel for people with severe paralysis. However, they require the presentation of visual stimuli in the field of view of the user. A head-mounted display could allow convenient presentation of visual stimuli in situations, where mounting a conventional monitor might be difficult or not feasible (e.g., at a patient's bedside). To explore if similar accuracies can be achieved with a virtual reality (VR) headset compared to a conventional flat screen monitor, we conducted an experiment with 18 healthy participants. We also evaluated it with a person in the locked-in state (LIS) to verify that usage of the headset is possible for a severely paralyzed person. Healthy participants performed online spelling with three different display methods. In one condition a 5 x 5 letter matrix was presented on a conventional 22 inch TFT monitor. Two configurations of the VR headset were tested. In the first (glasses A), the same 5 x 5 matrix filled the field of view of the user. In the second (glasses B), single letters of the matrix filled the field of view of the user. The participant in the LIS tested the VR headset on three different occasions (glasses A condition only). For healthy participants, average online spelling accuracies were 94% (15.5 bits/min) using three flash sequences for spelling with the monitor and glasses A and 96% (16.2 bits/min) with glasses B. In one session, the participant in the LIS reached an online spelling accuracy of 100% (10 bits/min) using the glasses A condition. We also demonstrated that spelling with one flash sequence is possible with the VR headset for healthy users (mean: 32.1 bits/min, maximum reached by one user: 71.89 bits/min at 100% accuracy). We conclude that the VR headset allows for rapid P300 BCI communication in healthy users and may be a suitable display option for severely paralyzed persons

Development of a SSVEP-BCI system for decision-making assistance

Orientadores: Gilmar Barreto, Linnyer Beatrys Ruiz AylonTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Nos últimos anos, Interfaces Cérebro-Computador (BCI) passaram a ter um maior foco em problemas fora do escopo clínico. Sistema BCI podem ser utilizados para controlar equipamentos elétricos e eletrônicos, controle de jogos digitais, etc. A capacidade de poder "controlar" em um sistema BCI, pode ser adaptada a uma ação que auxilia um indivíduo em tomada de decisões, por exemplo, decidir se paramos ou continuamos a conduzir um automóvel ao visualizar os estados de um semáforo de trânsito. O BCI baseado no paradigma de Potenciais Evocados Visualmente em Regime Estacionário (SSVEP), pode ser utilizado para diferenciar alvos com diferentes frequências de cintilação por meio de estímulos visuais. Esta tese de doutorado teve como objetivo avaliar o estímulo SSVEP de altas e baixas frequências admitas pelo paradigma, para a construção de um sistema SSVEP-BCI para auxiliar na tomada de decisões. Para cumprir com este objetivo, foram realizados (1) experimentos com uma base de dados pública com estímulos SSVEP armazenados, para avaliar os códigos desenvolvidos, (2) construção de uma base de dados gerada por meio de experimentos realizados com um protótipo de semáforo de trânsito, para avaliar o funcionamento do protótipo e do equipamento de eletroencefalografia (EEG) e, por fim, (3) experimentos realizados com quatro participantes para avaliar os estímulos SSVEP em baixas frequências de cintilação, tradicionalmente utilizadas do paradigma e altas frequências de cintilação configuradas em um limiar não visível aos nossos olhos, permitindo que o protótipo se comporte de forma mais próxima a situações reais e ainda forneça uma menor fadiga visual. Os resultados obtidos forneceram a exatidão dos programas desenvolvidos para avaliar os estímulos SSVEP e também o funcionamento do protótipo e do equipamento de EEG. Além disso, os experimentos realizados com os quatro participantes apresentaram em média uma acurácia de 89,37%±8,26% para baixas frequências e 80,62%±7,18% para altas frequências, no qual concluímos que o sistema SSVEP-BCI pode ser utilizado para auxiliar em situações de tomada de decisão em ambas as faixas de frequênciaAbstract: In recent years, Brain-Computer Interfaces (BCI) have an increased focus on problems outside the clinical scope. BCI system can be used to control electrical and electronic equipment, control of digital games, etc. The ability to "control" in a BCI system can be adapted to an action that assists an individual in decision-making, for example, deciding whether to stop or continue driving a car when viewing the states of a traffic light. The BCI paradigm based on Stead-State Visually Evoked Potentials (SSVEP) can be used to differentiate targets with different frequencies of scintillation through visual stimuli. This PhD thesis aimed to evaluate the SSVEP stimulus of high and low frequencies admitted by the paradigm, for the construction of a SSVEP-BCI system to assist in decision-making. In order to comply with this objective, we performed (1) experiments with a public database with stored SSVEP stimuli to evaluate developed codes, (2) we constructed a database generated through experiments carried out with a traffic light prototype, to evaluate the functioning of the prototype and electroencephalography (EEG) equipment, and finally (3) experiments was performed with four participants to evaluate the SSVEP stimuli at low scintillation frequencies, traditionally used in the paradigm and high scintillation frequencies configured in a threshold not visible to our eyes, allowing the prototype to behave more closely to real-world situations and still provide less visual fatigue. The results obtained provided the correct execution of written programs to evaluate the SSVEP stimuli and also the functioning of the prototype and the EEG equipment. In addition, the results from the experiments carried out with the four participants presented on average an accuracy of 89.37%±8.26% for low frequencies and 80.62%±7.18% for high frequencies, in which we concluded that the SSVEP-BCI system can be used to assist in decision-making situations in both frequency bandsDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

Die Wirksamkeit von Feedback und Trainingseffekten während der Alphaband Modulation über dem menschlichen sensomotorischen Cortex

Neural oscillations can be measured by electroencephalography (EEG) and these oscillations can be characterized by their frequency, amplitude and phase. The mechanistic properties of neural oscillations and their synchronization are able to explain various aspects of many cognitive functions such as motor control, memory, attention, information transfer across brain regions, segmentation of the sensory input and perception (Arnal and Giraud, 2012). The alpha band frequency is the dominant oscillation in the human brain. This oscillatory activity is found in the scalp EEG at frequencies around 8-13 Hz in all healthy adults (Makeig et al., 2002) and considerable interest has been generated in exploring EEG alpha oscillations with regard to their role in cognitive (Klimesch et al., 1993; Hanselmayr et al., 2005), sensorimotor (Birbaumer, 2006; Sauseng et al., 2009) and physiological (Lehmann, 1971; Niedermeyer, 1997; Kiyatkin, 2010) aspects of human life. The ability to voluntarily regulate the alpha amplitude can be learned with neurofeedback training and offers the possibility to control a brain-computer interface (BCI), a muscle independent interaction channel. BCI research is predominantly focused on the signal processing, the classification and the algorithms necessary to translate brain signals into control commands than on the person interacting with the technical system. The end-user must be properly trained to be able to successfully use the BCI and factors such as task instructions, training, and especially feedback can therefore play an important role in learning to control a BCI (Neumann and Kübler, 2003; Pfurtscheller et al., 2006, 2007; Allison and Neuper, 2010; Friedrich et al., 2012; Kaufmann et al., 2013; Lotte et al., 2013). The main purpose of this thesis was to investigate how end-users can efficiently be trained to perform alpha band modulation recorded over their sensorimotor cortex. The herein presented work comprises three studies with healthy participants and participants with schizophrenia focusing on the effects of feedback and training time on cortical activation patterns and performance. In the first study, the application of a realistic visual feedback to support end-users in developing a concrete feeling of kinesthetic motor imagery was tested in 2D and 3D visualization modality during a single training session. Participants were able to elicit the typical event-related desynchronisation responses over sensorimotor cortex in both conditions but the most significant decrease in the alpha band power was obtained following the three-dimensional realistic visualization. The second study strengthen the hypothesis that an enriched visual feedback with information about the quality of the input signal supports an easier approach for motor imagery based BCI control and can help to enhance performance. Significantly better performance levels were measurable during five online training sessions in the groups with enriched feedback as compared to a conventional simple visual feedback group, without significant differences in performance between the unimodal (visual) and multimodal (auditory–visual) feedback modality. Furthermore, the last study, in which people with schizophrenia participated in multiple sessions with simple feedback, demonstrated that these patients can learn to voluntarily regulate their alpha band. Compared to the healthy group they required longer training times and could not achieve performance levels as high as the control group. Nonetheless, alpha neurofeedback training lead to a constant increase of the alpha resting power across all 20 training session. To date only little is known about the effects of feedback and training time on BCI performance and cortical activation patterns. The presented work contributes to the evidence that healthy individuals can benefit from enriched feedback: A realistic presentation can support participants in getting a concrete feeling of motor imagery and enriched feedback, which instructs participants about the quality of their input signal can give support while learning to control the BCI. This thesis demonstrates that people with schizophrenia can learn to gain control of their alpha oscillations recorded over the sensorimotor cortex when participating in sufficient training sessions. In conclusion, this thesis improved current motor imagery BCI feedback protocols and enhanced our understanding of the interplay between feedback and BCI performance.Die Wirksamkeit von Feedback und Trainingseffekten während der Alphaband Modulation über dem menschlichen sensomotorischen Corte