@article{kambhamettu2023brain,

title = {Brain-Computer Interface-Assisted Automated Wheelchair Control Management--Cerebro: A BCI Application},

author = {Sudhendra Kambhamettu and Meenalosini Vimal Cruz and S Anitha and S Sibi Chakkaravarthy and K Nandeesh Kumar},

doi = {https://doi.org/10.1002/9781119857655.ch9},

year  = {2023},

date = {2023-02-10},

urldate = {2023-01-01},

journal = {Brain-Computer Interface: Using Deep Learning Applications},

pages = {205--229},

publisher = {Wiley Online Library},

abstract = {Technology today serves millions of people suffering from mobility impairments across the globe in numerous ways. Although advancements in medicine and healthcare systems improve the life expectancy of the general population, sophisticated engineering techniques and computing processes have long facilitated the patient in the recovery process. People struggling with mobility impairments and especially spine injuries which also leads to loss of speech, often have a narrow group of devices to aid them move from place-to-place and they are often limited to just movement functionality. BCI (Brain Computer Interface) powered wheelchairs leverage the power of the brain, i.e. translating the thoughts/neural activity into real-world movement providing automated motion without any third party intervention. Many BCI powered wheelchairs in the market are cumbersome to operate and provide only singular functionality of movement. To address this problem and improve the state of BCI products, Cerebro introduces the first ever go-to market product utilizing Artificial Intelligence to facilitate mobility features with built-in speech functionality via blink detection. Further sections of the Chapter take an in-depth look into each layer of the Cerebro system.



},

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}

@article{won2022can,

title = {Can Vibrotactile Stimulation and tDCS Help Inefficient BCI Users?},

author = {Kyungho Won and Heegyu Kim and Daeun Gwon and Minkyu Ahn and Chang S Nam and Sung Chan Jun},

doi = {https://doi.org/10.21203/rs.3.rs-1849849/v1},

year  = {2022},

date = {2022-07-22},

urldate = {2022-07-22},

abstract = {Brain-computer interface (BCI) has helped people by enabling them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and comparative study of different stimulation modalities has been overlooked. Accordingly, this comparative study was designed to investigate vibrotactile stimulation and transcranial direct current stimulation’s (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the BCI performance of low performers in the vibrotactile stimulation group increased significantly by 9.13% (p=0.0053), and while the tDCS group subjects’ performance increased by 5.13%, it was not significant. In contrast, sham group subjects showed no increased performance. In addition to BCI performance, pre-stimulus alpha band power and the phase locking value (PLVs) averaged over sensory motor areas showed significant increases in low performers following stimulation in the vibrotactile stimulation and tDCS groups, while sham stimulation group subjects and high performers across all groups showed no significant stimulation effects. Our findings suggest that stimulation effects may differ depending upon BCI efficiency, and inefficient BCI users have greater plasticity than efficient BCI users.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{humphries2022motor,

title = {Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface},

author = {Joseph B Humphries and Daniela JS Mattos and Jerrel Rutlin and Andy GS Daniel and Kathleen Rybczynski and Theresa Notestine and Joshua S Shimony and Harold Burton and Alexandre Carter and Eric C Leuthardt},

doi = {https://doi.org/10.1080/2326263X.2022.2057757},

year  = {2022},

date = {2022-07-01},

urldate = {2022-01-01},

journal = {Brain-Computer Interfaces},

volume = {9},

number = {3},

pages = {179--192},

publisher = {Taylor & Francis},

abstract = {Upper extremity weakness in chronic stroke remains a problem not fully addressed by current therapies. Brain–computer interfaces (BCIs) engaging the unaffected hemisphere are a promising therapy that are entering clinical application, but the mechanism underlying recovery is not well understood. We used resting state functional MRI to assess the impact a contralesionally driven EEG BCI therapy had on motor system functional organization. Patients used a therapeutic BCI for 12 weeks at home. We acquired resting-state fMRI scans and motor function data before and after the therapy period. Changes in functional connectivity (FC) strength between motor network regions of interest (ROIs) and the topographic extent of FC to specific ROIs were analyzed. Most patients achieved clinically significant improvement. Motor FC strength and topographic extent decreased following BCI therapy. Motor recovery correlated with reductions in motor FC strength across the entire motor network. These findings suggest BCI-mediated interventions may reverse pathologic strengthening of dysfunctional network interactions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kim2022brain,

title = {Brain--computer interface-based action observation combined with peripheral electrical stimulation enhances corticospinal excitability in healthy subjects and stroke patients},

author = {Min Gyu Kim and Hyunmi Lim and Hye Sun Lee and In Jun Han and Jeonghun Ku and Youn Joo Kang},

url = {https://iopscience.iop.org/article/10.1088/1741-2552/ac76e0/meta?casa_token=MPuDFAHtwF4AAAAA:Q_cSc8qcY0m6fnqiqPpkHv5cAIzKaJBw51nYjwygju0LbXYaujodUGwUy1RjTcbCm-MTN7ZnOg},

year  = {2022},

date = {2022-06-20},

urldate = {2022-01-01},

journal = {Journal of Neural Engineering},

volume = {19},

number = {3},

publisher = {IOP Publishing},

abstract = {Objective. Action observation (AO) combined with brain–computer interface (BCI) technology enhances cortical activation. Peripheral electrical stimulation (PES) increases corticospinal excitability, thereby activating brain plasticity. To maximize motor recovery, we assessed the effects of BCI-AO combined with PES on corticospinal plasticity. Approach. Seventeen patients with chronic hemiplegic stroke and 17 healthy subjects were recruited. The participants watched a video of repetitive grasping actions with four different tasks for 15 min: (A) AO alone; (B) AO + PES; (C) BCI-AO + continuous PES; and (D) BCI-AO + triggered PES. PES was applied at the ulnar nerve of the wrist. The tasks were performed in a random order at least three days apart. We assessed the latency and amplitude of motor evoked potentials (MEPs). We examined changes in MEP parameters pre-and post-exercise across the four tasks in the first dorsal interosseous muscle of the dominant hand (healthy subjects) and affected hand (stroke patients). Main results. The decrease in MEP latency and increase in MEP amplitude after the four tasks were significant in both groups. The increase in MEP amplitude was sustained for 20 min after tasks B, C, and D in both groups. The increase in MEP amplitude was significant between tasks A vs. B, B vs. C, and C vs. D. The estimated mean difference in MEP amplitude post-exercise was the highest for A and D in both groups. Significance. The results indicate that BCI-AO combined with PES is superior to AO alone or AO + PES for facilitating corticospinal plasticity in both healthy subjects and patients with stroke. Furthermore, this study supports the idea that synchronized activation of cortical and peripheral networks can enhance neuroplasticity after stroke. We suggest that the BCI-AO paradigm and PES could provide a novel neurorehabilitation strategy for patients with stroke.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rustamov2022thetab,

title = {Theta-gamma coupling as a cortical biomarker of brain-computer interface mediated motor recovery in chronic stroke},

author = {Nabi Rustamov and Joseph Humphries and Alexandre Carter and Eric C Leuthardt},

doi = {https://doi.org/10.1093/braincomms/fcac136},

year  = {2022},

date = {2022-05-25},

urldate = {2022-01-01},

journal = {Brain Communications},

volume = {4},

issue = {3},

abstract = {Chronic stroke patients with upper-limb motor disabilities are now beginning to see treatment options that were not previously available. To date, the two options recently approved by the United States Food and Drug Administration include vagus nerve stimulation and brain–computer interface therapy. While the mechanisms for vagus nerve stimulation have been well defined, the mechanisms underlying brain–computer interface-driven motor rehabilitation are largely unknown. Given that cross-frequency coupling has been associated with a wide variety of higher-order functions involved in learning and memory, we hypothesized this rhythm-specific mechanism would correlate with the functional improvements effected by a brain–computer interface. This study investigated whether the motor improvements in chronic stroke patients induced with a brain–computer interface therapy are associated with alterations in phase–amplitude coupling, a type of cross-frequency coupling. Seventeen chronic hemiparetic stroke patients used a robotic hand orthosis controlled with contralesional motor cortical signals measured with EEG. Patients regularly performed a therapeutic brain–computer interface task for 12 weeks. Resting-state EEG recordings and motor function data were acquired before initiating brain–computer interface therapy and once every 4 weeks after the therapy. Changes in phase–amplitude coupling values were assessed and correlated with motor function improvements. To establish whether coupling between two different frequency bands was more functionally important than either of those rhythms alone, we calculated power spectra as well. We found that theta–gamma coupling was enhanced bilaterally at the motor areas and showed significant correlations across brain–computer interface therapy sessions. Importantly, an increase in theta–gamma coupling positively correlated with motor recovery over the course of rehabilitation. The sources of theta–gamma coupling increase following brain–computer interface therapy were mostly located in the hand regions of the primary motor cortex on the left and right cerebral hemispheres. Beta–gamma coupling decreased bilaterally at the frontal areas following the therapy, but these effects did not correlate with motor recovery. Alpha–gamma coupling was not altered by brain–computer interface therapy. Power spectra did not change significantly over the course of the brain–computer interface therapy. The significant functional improvement in chronic stroke patients induced by brain–computer interface therapy was strongly correlated with increased theta–gamma coupling in bihemispheric motor regions. These findings support the notion that specific cross-frequency coupling dynamics in the brain likely play a mechanistic role in mediating motor recovery in the chronic phase of stroke recovery.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{klee2022target,

title = {Target-Related Alpha Attenuation in a Brain-Computer Interface Rapid Serial Visual Presentation Calibration},

author = {Daniel Klee and Tab Memmott and Niklas Smedemark-Margulies and Basak Celik and Deniz Erdogmus and Barry S Oken},

doi = {10.3389/fnhum.2022.882557},

year  = {2022},

date = {2022-04-21},

urldate = {2022-01-01},

journal = {Frontiers in Human Neuroscience},

volume = {16},

publisher = {Frontiers Media SA},

abstract = {This study evaluated the feasibility of using occipitoparietal alpha activity to drive target/non-target classification in a brain-computer interface (BCI) for communication. EEG data were collected from 12 participants who completed BCI Rapid Serial Visual Presentation (RSVP) calibrations at two different presentation rates: 1 and 4 Hz. Attention-related changes in posterior alpha activity were compared to two event-related potentials (ERPs): N200 and P300. Machine learning approaches evaluated target/non-target classification accuracy using alpha activity. Results indicated significant alpha attenuation following target letters at both 1 and 4 Hz presentation rates, though this effect was significantly reduced in the 4 Hz condition. Target-related alpha attenuation was not correlated with coincident N200 or P300 target effects. Classification using posterior alpha activity was above chance and benefitted from individualized tuning procedures. These findings suggest that target-related posterior alpha attenuation is detectable in a BCI RSVP calibration and that this signal could be leveraged in machine learning algorithms used for RSVP or comparable attention-based BCI paradigms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kim2021p300,

title = {P300 Brain--Computer Interface-Based Drone Control in Virtual and Augmented Reality},

author = {Soram Kim and Seungyun Lee and Hyunsuk Kang and Sion Kim and Minkyu Ahn},

doi = {https://doi.org/10.3390/s21175765},

year  = {2021},

date = {2021-08-27},

journal = {Sensors},

volume = {21},

number = {17},

pages = {5765},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Since the emergence of head-mounted displays (HMDs), researchers have attempted to introduce virtual and augmented reality (VR, AR) in brain–computer interface (BCI) studies. However, there is a lack of studies that incorporate both AR and VR to compare the performance in the two environments. Therefore, it is necessary to develop a BCI application that can be used in both VR and AR to allow BCI performance to be compared in the two environments. In this study, we developed an opensource-based drone control application using P300-based BCI, which can be used in both VR and AR. Twenty healthy subjects participated in the experiment with this application. They were asked to control the drone in two environments and filled out questionnaires before and after the experiment. We found no significant (p > 0.05) difference in online performance (classification accuracy and amplitude/latency of P300 component) and user experience (satisfaction about time length, program, environment, interest, difficulty, immersion, and feeling of self-control) between VR and AR. This indicates that the P300 BCI paradigm is relatively reliable and may work well in various situations},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lim2021superior,

title = {Superior Facilitation of an Action Observation Network by Congruent Character Movements in Brain--Computer Interface Action-Observation Games},

author = {Hyunmi Lim and Jeonghun Ku},

doi = {https://doi.org/10.1089/cyber.2020.0231},

year  = {2021},

date = {2021-08-04},

journal = {Cyberpsychology, Behavior, and Social Networking},

volume = {24},

number = {8},

pages = {566--572},

publisher = {Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~…},

abstract = {Action observation (AO) is a promising strategy for promoting motor function in neural rehabilitation. Recently, brain–computer interface (BCI)-AO game rehabilitation, which combines AO therapy with BCI technology, has been introduced to improve the effectiveness of rehabilitation. This approach can improve motor learning by providing feedback, which can be interactive in an observation task, and the game contents of the BCI-AO game paradigm can affect rehabilitation. In this study, the effects of congruent rather than incongruent feedback in a BCI-AO game on mirror neurons were investigated. Specifically, the mu suppression with congruent and incongruent BCI-AO games was measured in 17 healthy adults. The mu suppression in the central motor cortex was significantly higher with the congruent BCI-AO game than with the incongruent one. In addition, the satisfaction evaluation results were excellent for the congruent case. These results support the fact that providing feedback congruent with the motion of an action video facilitates mirror neuron activity and can offer useful guidelines for the design of BCI-AO games for rehabilitation},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{eldeeb2021trial,

title = {Trial by trial EEG based BCI for distress versus non distress classification in individuals with ASD},

author = {Safaa Eldeeb and Busra T Susam and Murat Akcakaya and Caitlin M Conner and Susan W White and Carla A Mazefsky},

url = {https://www.nature.com/articles/s41598-021-85362-8},

year  = {2021},

date = {2021-03-16},

journal = {Scientific Reports},

volume = {11},

number = {1},

pages = {1--13},

publisher = {Nature Publishing Group},

abstract = {Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).},

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pubstate = {published},

tppubtype = {article}

}