324 research outputs found

    A Brief Exposition on Brain-Computer Interface

    Get PDF
    Brain-Computer Interface is a technology that records brain signals and translates them into useful commands to operate a drone or a wheelchair. Drones are used in various applications such as aerial operations, where pilot’s presence is impossible. The BCI can also be used for patients suffering from brain diseases who lose their body control and are unable to move to satisfy their basic needs. By taking advantage of BCI and drone technology, algorithms for Mind-Controlled Unmanned Aerial System can be developed. This paper deals with the classification of BCI & UAV, methodologies of BCI, the framework of BCI, neuro-imaging methods, BCI headset options, BCI platforms, electrode types & their placement, and the result of feature extraction technique (FFT) with 72.5% accuracy

    A screening protocol incorporating brain-computer interface feature matching considerations for augmentative and alternative communication

    Get PDF
    Purpose: The use of standardized screening protocols may inform brain-computer interface (BCI) research procedures to help maximize BCI performance outcomes and provide foundational information for clinical translation. Therefore, in this study we developed and evaluated a new BCI screening protocol incorporating cognitive, sensory, motor and motor imagery tasks. Methods: Following development, BCI screener outcomes were compared to the Amyotrophic Lateral Sclerosis Cognitive Behavioral Screen (ALS-CBS), and ALS Functional Rating Scale (ALS-FRS) for twelve individuals with a neuromotor disorder. Results: Scores on the cognitive portion of the BCI screener demonstrated limited variability, indicating all participants possessed core BCI-related skills. When compared to the ALS-CBS, the BCI screener was able to modestly discriminate possible cognitive difficulties that are likely to influence BCI performance. In addition, correlations between the motor imagery section of the screener and ALS-CBS and ALS-FRS were non-significant, suggesting the BCI screener may provide information not captured on other assessment tools. Additional differences were found between motor imagery tasks, with greater self-ratings on first-person explicit imagery of familiar tasks compared to unfamiliar/ generic BCI tasks. Conclusion: The BCI screener captures factors likely relevant for BCI, which has value for guiding person-centered BCI assessment across different devices to help inform BCI trials. Includes supplemental data

    Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification

    Full text link
    Objective. The main goal of this work is to develop a model for multi-sensor signals such as MEG or EEG signals, that accounts for the inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI type experiments. Approach. The method involves linear mixed effects statistical model, wavelet transform and spatial filtering, and aims at the characterization of localized discriminant features in multi-sensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e. discriminant) and background noise, using a very simple Gaussian linear mixed model. Main results. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data, in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. Significance. The combination of linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves on earlier results on similar problems, and the three main ingredients all play an important role

    Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

    Get PDF
    Sparse methods are widely used in image and audio processing for denoising and classification, but there have been few previous applications to neural signals for brain-computer interfaces (BCIs). We used the dictionary- learning algorithm K-SVD, coupled with Orthogonal Matching Pursuit, to learn dictionaries of spatial and temporal EEG primitives. We applied these to P300 and ErrP data to denoise the EEG and better estimate the underlying P300 and ErrP signals. This methodology improved single-trial classification performance across 13 of 14 subjects, indicating that some of the background noise in EEG signals, presumably from neural or muscular sources, is highly structured. Furthermore, this structure can be captured via dictionary learning and sparse coding algorithms, and exploited to improve BCIs

    Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals

    Get PDF
    Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of information when features are well-aligned across signals. This is the case, for instance, in multi-trial magneto- or electroencephalography (M/EEG). Learning the dictionary on the entire signals could make use of the alignement and reveal higher-level features. In this case, however, small missalignements or phase variations of features would not be compensated for. In this paper, we propose an extension to the common dictionary learning framework to overcome these limitations by allowing atoms to adapt their position across signals. The method is validated on simulated and real neuroelectric data.Comment: 9 pages, 5 figures, minor correction

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

    Get PDF
    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

    Neurogaming With Motion-Onset Visual Evoked Potentials (mVEPs): Adults Versus Teenagers

    Get PDF
    corecore