Browsing by Subject "Brain Computer Interface"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Ballistic Finger Imagery for Asynchronous BCI Control
    (2017-12) Suma, Daniel
    In this work a novel paradigm and algorithm are developed in order to decode ballistic finger imagery of both the left and right index fingers in real time. The novel paradigm is specifically developed for rapid decision making and high information transfer rates. This is done through a hybrid classifier as well as the use of tripolar electro-encephalography (TEEG). TEEG cortical signals are also compared with concurrently recorded traditional EEG signals (Au), as well as high density EEG signals (Ag/AgCl). Online results are shown to be stable across 10 subjects over three sessions. Spatio-temporal analysis is performed in both the sensor and electro-cortical source domain offline and additional features are derived through a combination of data-mining and neuroscientific insight to develop a robust TEEG ERP derived classifier which achieves 88.8% accuracy in a subject independent monte-carlo cross validation simulation. TEEG signals are found to have similar waveforms and spatial maps as EEG signals, but are found to be more independent with low or often negative correlations with other channels, as well as diverse covariance matrices.
  • Thumbnail Image
    Item
    Left-handed BCI - examining effects of handedness and hand dominance on EEG grasp classification
    (2022-12) Dowling, Dale
    Brain-Computer Interfaces (BCIs) are of high potential use to individuals whosemotor function is impaired, or who have undergone a loss of limb or limb functionality. Electroencephalography (EEG) is one popular method of collecting signals from the brain, and is commonly used in cases where other sensing methods are difficult or impossible. This method of collecting brain signal data has been shown, when used in conjunction with Electromyography (EMG) data, to be capable of classifying fine hand movements with a high degree of accuracy, providing avenues for the design of highly attenuated prosthetic limbs. Studies which have examined such uses for BCIs, however, seldom examine the effects of handedness, as well as off-hand or dualhanded motion, on classification accuracy. This study examines the effects of hand use and hand dominance on the performance of several classifiers derived from EEG data. Data was collected, using the OpenBCI EEG Electrode Cap Kit, for 16 participants (9 right-handed, 7 left-handed), on a set of 6 grasp types, and a selection of 5 classification algorithms (Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine, and Neural Network) commonly found in previous works were used. Outcomes of the study indicate that Neural Networks are best suited among these classifiers to determine hand motion in a dual-handed environment, and that, while providing hand-dominance data for classification training may not improve accuracy in all cases, design and feature changes based on factors such as hand-dominance may improve the performance of BCIs based on EEG data.
  • Thumbnail Image
    Item
    Targeting the Brain in Brain-Computer Interfacing: The Effect of Transcranial Current Stimulation and Control of a Physical Effector on Performance and Electrophysiology Underlying Noninvasive Brain-Computer Interfaces
    (2017-07) Baxter, Bryan
    Brain-computer interfaces (BCIs) and neuromodulation technologies have recently begun to fulfill their promises of restoring function, improving rehabilitation, and enhancing abilities and learning. However, lengthy user training to achieve acceptable accuracy is a barrier to BCI acceptance and use by patients and the general population. Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation technology whereby a low level of electrical current is injected into the brain to alter neural activity and has been found to improve motor learning and task performance. A barrier to optimizing behavioral effects of tDCS is that we do not yet understand how neural networks are affected by stimulation and how stimulation interacts with ongoing endogenous activity. The purpose of this dissertation was to elucidate strategies to improve BCI control by targeting the user through two approaches: 1. Subject control of a robotic arm to enhance user motivation and 2. tDCS application to improve behavioral outcomes and alter networks underlying sensorimotor rhythm-based BCI performance. The primary results illustrate that targeted tDCS of the motor network interacts with task specific neural activity to improve BCI performance and alter neural electrophysiology. This effect on neural activity extended across the task network, beyond the area of direct stimulation, and altered connectivity unilaterally and bilaterally between frontal and parietal cortical regions. These findings suggest targeted neuromodulation interacts with endogenous neural activity and can be used to improve motor-cognitive task performance.