Bachelor of Science, Carnegie Mellon University (2012)
Doctor of Philosophy, University of Minnesota Twin Cities (2018)
Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
2018; 26 (5): 936–47
EEG-based brain-computer interface (BCI) technology creates non-biological pathways for conveying a user's mental intent solely through noninvasively measured neural signals. While optimizing the performance of a single task has long been the focus of BCI research, in order to translate this technology into everyday life, realistic situations, in which multiple tasks are performed simultaneously, must be investigated. In this paper, we explore the concept of cognitive flexibility, or multitasking, within the BCI framework by utilizing a 2-D cursor control task, using sensorimotor rhythms (SMRs), and a four-target visual attention task, using steady-state visual evoked potentials (SSVEPs), both individually and simultaneously. We found no significant difference between the accuracy of the tasks when executing them alone (SMR-57.9% ± 15.4% and SSVEP-59.0% ± 14.2%) and simultaneously (SMR-54.9% ± 17.2% and SSVEP-57.5% ± 15.4%). These modest decreases in performance were supported by similar, non-significant changes in the electrophysiology of the SSVEP and SMR signals. In this sense, we report that multiple BCI tasks can be performed simultaneously without a significant deterioration in performance; this finding will help drive these systems toward realistic daily use in which a user's cognition will need to be involved in multiple tasks at once.
View details for DOI 10.1109/TNSRE.2018.2817924
View details for PubMedID 29752228
EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2016; 63 (1): 4-14
Sensorimotor-based brain-computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device.We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation.We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method.ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks.This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.
View details for DOI 10.1109/TBME.2015.2467312
View details for Web of Science ID 000369311700002
View details for PubMedID 26276986
View details for PubMedCentralID PMC4716869
Systems neuroengineering: Understanding and interacting with the brain
2015; 1 (3): 292-308
View details for DOI 10.15302/J-ENG-2015078
A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance
FRONTIERS IN NEUROSCIENCE
2018; 12: 227
Motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session-performance increases asymptotically by increasing the number of channels, saturates, and then decreases-no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method.
View details for DOI 10.3389/fnins.2018.00227
View details for Web of Science ID 000429368500001
View details for PubMedID 29681792
View details for PubMedCentralID PMC5897442
Combined rTMS and virtual reality brain-computer interface training for motor recovery after stroke
Journal of Neural Engineering
View details for DOI 10.1088/1741-2552/aa8ce3
Anodal Transcranial Direct Current Stimulation Increases Bilateral Directed Brain Connectivity during Motor-Imagery Based Brain-Computer Interface Control.
Frontiers in neuroscience
2017; 11: 691
Transcranial direct current stimulation (tDCS) has been shown to affect motor and cognitive task performance and learning when applied to brain areas involved in the task. Targeted stimulation has also been found to alter connectivity within the stimulated hemisphere during rest. However, the connectivity effect of the interaction of endogenous task specific activity and targeted stimulation is unclear. This study examined the aftereffects of concurrent anodal high-definition tDCS over the left sensorimotor cortex with motor network connectivity during a one-dimensional EEG based sensorimotor rhythm brain-computer interface (SMR-BCI) task. Directed connectivity following anodal tDCS illustrates altered connections bilaterally between frontal and parietal regions, and these alterations occur in a task specific manner; connections between similar cortical regions are altered differentially during left and right imagination trials. During right-hand imagination following anodal tDCS, there was an increase in outflow from the left premotor cortex (PMC) to multiple regions bilaterally in the motor network and increased inflow to the stimulated sensorimotor cortex from the ipsilateral PMC and contralateral sensorimotor cortex. During left-hand imagination following anodal tDCS, there was increased outflow from the stimulated sensorimotor cortex to regions across the motor network. Significant correlations between connectivity and the behavioral measures of total correct trials and time-to-hit (TTH) correct trials were also found, specifically that the input to the left PMC correlated with decreased right hand imagination performance and that flow from the ipsilateral posterior parietal cortex (PPC) to midline sensorimotor cortex correlated with improved performance for both right and left hand imagination. These results indicate that tDCS interacts with task-specific endogenous activity to alter directed connectivity during SMR-BCI. In order to predict and maximize the targeted effect of tDCS, the interaction of stimulation with the dynamics of endogenous activity needs to be examined comprehensively and understood.
View details for DOI 10.3389/fnins.2017.00691
View details for PubMedID 29270110
View details for PubMedCentralID PMC5725434
Sensorimotor Rhythm BCI with Simultaneous High Definition-Transcranial Direct Current Stimulation Alters Task Performance
2016; 9 (6): 834-841
Transcranial direct current stimulation (tDCS) has been used to alter the excitability of neurons within the cerebral cortex. Improvements in motor learning have been found in multiple studies when tDCS was applied to the motor cortex before or during task learning. The motor cortex is also active during the performance of motor imagination, a cognitive task during which a person imagines, but does not execute, a movement. Motor imagery can be used with noninvasive brain computer interfaces (BCIs) to control virtual objects in up to three dimensions, but to master control of such devices requires long training times.To evaluate the effect of high-definition tDCS on the performance and underlying electrophysiology of motor imagery based BCI.We utilize high-definition tDCS to investigate the effect of stimulation on motor imagery-based BCI performance across and within sessions over multiple training days.We report a decreased time-to-hit with anodal stimulation both within and across sessions. We also found differing electrophysiological changes of the stimulated sensorimotor cortex during online BCI task performance for left vs. right trials. Cathodal stimulation led to a decrease in alpha and beta band power during task performance compared to sham stimulation for right hand imagination trials.These results suggest that unilateral tDCS over the sensorimotor motor cortex differentially affects cortical areas based on task specific neural activation.
View details for DOI 10.1016/j.brs.2016.07.003
View details for Web of Science ID 000387197500005
View details for PubMedID 27522166
View details for PubMedCentralID PMC5143161
- Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms PROCEEDINGS OF THE IEEE 2015; 103 (6): 907-925
Prolonged cortical silent period among drug-naive subjects at ultra-high risk of psychosis
2014; 160 (1-3): 124-130
Deficits in gamma-aminobutyric acid (GABA) inhibitory neurotransmission have been associated with pathophysiological mechanisms underlying schizophrenia. However, little is known about whether these deficits occur before or after the onset of psychosis.We recruited 16 drug-naive subjects at ultra-high risk of psychosis (UHR), 17 schizophrenia patients and 28 healthy controls. Cortical inhibition was determined using transcranial magnetic stimulation (TMS) over the left primary motor cortex. TMS markers such as short-interval cortical inhibition (SICI), cortical silent period (CSP) and intracortical facilitation (ICF) were obtained from each subject. While SICI can reflect GABA type A (GABAA) mediated inhibition, CSP is thought to indicate GABA type B (GABAB) mediated inhibitory circuits.As compared with healthy controls, UHR subjects showed a prolonged CSP with no change in SICI, whereas schizophrenia patients demonstrated both a prolonged CSP and a reduced SICI. No group differences were found for ICF. CSP in schizophrenia patients also had a positive correlation with positive symptom score of the positive and negative symptom scale (PANSS).Cortical inhibitory deficits among UHR subjects were relatively limited compared to those among schizophrenia patients. Alterations might occur in some subgroup of GABA-mediated neurotransmitter systems before the onset of psychosis, while alterations in both GABAA and GABAB networks might contribute to full-blown psychosis.
View details for DOI 10.1016/j.schres.2014.10.004
View details for Web of Science ID 000345961300036
View details for PubMedID 25458861