3,540 research outputs found
Calibration-Free BCI Based Control
Recent works have explored the use of brain signals to directly control virtual and robotic agents in sequential tasks. So far in such brain-computer interfaces (BCI), an explicit calibration phase was required to build a decoder that translates raw electroencephalography (EEG) signals from the brain of each user into meaningful instructions. This paper proposes a method that removes the calibration phase, and allows a user to control an agent to solve a sequential task. The proposed method assumes a distribution of possible tasks, and infers the interpretation of EEG signals and the task by selecting the hypothesis which best explains the history of interaction. We introduce a measure of uncertainty on the task and on the EEG signal interpretation to act as an exploratory bonus for a planning strategy. This speeds up learning by guiding the system to regions that better disambiguate among task hypotheses. We report experiments where four users use BCI to control an agent on a virtual world to reach a target without any previous calibration process
Brain-Computer Interface meets ROS: A robotic approach to mentally drive telepresence robots
This paper shows and evaluates a novel approach to integrate a non-invasive
Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to
mentally drive a telepresence robot. Controlling a mobile device by using human
brain signals might improve the quality of life of people suffering from severe
physical disabilities or elderly people who cannot move anymore. Thus, the BCI
user is able to actively interact with relatives and friends located in
different rooms thanks to a video streaming connection to the robot. To
facilitate the control of the robot via BCI, we explore new ROS-based
algorithms for navigation and obstacle avoidance, making the system safer and
more reliable. In this regard, the robot can exploit two maps of the
environment, one for localization and one for navigation, and both can be used
also by the BCI user to watch the position of the robot while it is moving. As
demonstrated by the experimental results, the user's cognitive workload is
reduced, decreasing the number of commands necessary to complete the task and
helping him/her to keep attention for longer periods of time.Comment: Accepted in the Proceedings of the 2018 IEEE International Conference
on Robotics and Automatio
True zero-training brain-computer interfacing: an online study
Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model
Playing with your mind
A Brain-Computer Interface (BCI) is a communication system between the brainand a machine like a computer. Some BCI systems have been used to help people withdisabilities and sometimes, with entertainment purposes. In this paper, a BCI-game system is developed. It allows controlling the altitude of a ball inside of a glass pipe according to mental concentration level, which is measured on EEG signals of the user. The system is automatically adjusted to each user, hence, it is not needed any calibration step. Ten subjects participated in the experiments. They achieved effective control of the ball in a few minutes, demonstratingthe feasibility of the BCI-game system.Fil: Rodriguez, Mauro. Universidad Nacional de San Juan. Facultad de IngenierĂa. Departamento de ElectrĂłnica y AutomĂĄtica. Gabinete de TecnologĂa MĂ©dica; ArgentinaFil: Gimenez, Ramiro. Universidad Nacional de San Juan. Facultad de IngenierĂa. Departamento de ElectrĂłnica y AutomĂĄtica. Gabinete de TecnologĂa MĂ©dica; ArgentinaFil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de IngenierĂa. Departamento de ElectrĂłnica y AutomĂĄtica. Gabinete de TecnologĂa MĂ©dica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Avila Perona, Enrique Mario. Universidad Nacional de San Juan. Facultad de IngenierĂa. Departamento de ElectrĂłnica y AutomĂĄtica. Gabinete de TecnologĂa MĂ©dica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Laciar Leber, Eric. Universidad Nacional de San Juan. Facultad de IngenierĂa. Departamento de ElectrĂłnica y AutomĂĄtica. Gabinete de TecnologĂa MĂ©dica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Orosco, Lorena Liliana. Universidad Nacional de San Juan. Facultad de IngenierĂa. Departamento de ElectrĂłnica y AutomĂĄtica. Gabinete de TecnologĂa MĂ©dica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas; ArgentinaFil: Garces, Agustina. Universidad Nacional de San Juan. Facultad de IngenierĂa. Departamento de ElectrĂłnica y AutomĂĄtica. Gabinete de TecnologĂa MĂ©dica; Argentin
Toward a semi-self-paced EEG brain computer interface: decoding initiation state from non-initiation state in dedicated time slots.
Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are "synchronous" systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in "asynchronous" BCIs subjects pace the interaction and the system must determine when the subject's control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject's intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs
Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns
Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands
to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphyâ
EEG), which is processed while they perform specific mental tasks. While very
promising, MI-BCIs remain barely used outside laboratories because of the difficulty
encountered by users to control them. Indeed, although some users obtain good control
performances after training, a substantial proportion remains unable to reliably control an
MI-BCI. This huge variability in user-performance led the community to look for predictors of
MI-BCI control ability. However, these predictors were only explored for motor-imagery
based BCIs, and mostly for a single training session per subject. In this study, 18 participants
were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2
of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships
between the participantsâ BCI control performances and their personality, cognitive
profile and neurophysiological markers were explored. While no relevant relationships with
neurophysiological markers were found, strong correlations between MI-BCI performances
and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive
model of MI-BCI performance based on psychometric questionnaire scores was proposed.
A leave-one-subject-out cross validation process revealed the stability and reliability of this
model: it enabled to predict participantsâ performance with a mean error of less than 3
points. This study determined how usersâ profiles impact their MI-BCI control ability and
thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of
each user
Would Motor-Imagery based BCI user training benefit from more women experimenters?
Mental Imagery based Brain-Computer Interfaces (MI-BCI) are a mean to control
digital technologies by performing MI tasks alone. Throughout MI-BCI use, human
supervision (e.g., experimenter or caregiver) plays a central role. While
providing emotional and social feedback, people present BCIs to users and
ensure smooth users' progress with BCI use. Though, very little is known about
the influence experimenters might have on the results obtained. Such influence
is to be expected as social and emotional feedback were shown to influence
MI-BCI performances. Furthermore, literature from different fields showed an
experimenter effect, and specifically of their gender, on experimental outcome.
We assessed the impact of the interaction between experi-menter and participant
gender on MI-BCI performances and progress throughout a session. Our results
revealed an interaction between participants gender, experimenter gender and
progress over runs. It seems to suggest that women experimenters may positively
influence partici-pants' progress compared to men experimenters
Interactive Reading Using Low Cost Brain Computer Interfaces
This work shows the feasibility for document reader user applications using a consumer grade non-invasive BCI headset. Although Brain Computer Interface (BCI) type devices are beginning to aim at the consumer level, the level at which they can actually detect brain activity is limited. There is however progress achieved in allowing for interaction between a human and a computer when this interaction is limited to around 2 actions. We employed the Emotiv Epoc, a low-priced BCI headset, to design and build a proof-of-concept document reader system that allows users to navigate the document using this low cast BCI device. Our prototype has been implemented and evaluated with 12 participants who were trained to navigate documents using signals acquired by Emotive Epoc
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