@article{gwon2024motor,

title = {Motor task-to-task transfer learning for motor imagery brain-computer interfaces},

author = {Daeun Gwon and Minkyu Ahn},

doi = {https://doi.org/10.1016/j.neuroimage.2024.120906},

year  = {2024},

date = {2024-10-28},

urldate = {2024-01-01},

journal = {NeuroImage},

pages = {120906},

publisher = {Elsevier},

abstract = {Motor imagery (MI) is one of the popular control paradigms in the non-invasive brain-computer interface (BCI) field. MI-BCI generally requires users to conduct the imagination of movement (e.g., left or right hand) to collect training data for generating a classification model during the calibration phase. However, this calibration phase is generally time-consuming and tedious, as users conduct the imagination of hand movement several times without being given feedback for an extended period. This obstacle makes MI-BCI non user-friendly and hinders its use. On the other hand, motor execution (ME) and motor observation (MO) are relatively easier tasks, yield lower fatigue than MI, and share similar neural mechanisms to MI. However, few studies have integrated these three tasks into BCIs. In this study, we propose a new task-to-task transfer learning approach of 3-motor tasks (ME, MO, and MI) for building a better user-friendly MI-BCI. For this study, 28 subjects participated in 3-motor tasks experiment, and electroencephalography (EEG) was acquired. User opinions regarding the 3-motor tasks were also collected through questionnaire survey. The 3-motor tasks showed a power decrease in the alpha rhythm, known as event-related desynchronization, but with slight differences in the temporal patterns. In the classification analysis, the cross-validated accuracy (within-task) was 67.05 % for ME, 65.93 % for MI, and 73.16 % for MO on average. Consistently with the results, the subjects scored MI (3.16) as the most difficult task compared with MO (1.42) and ME (1.41), with p < 0.05. In the analysis of task-to-task transfer learning, where training and testing are performed using different task datasets, the ME–trained model yielded an accuracy of 65.93 % (MI test), which is statistically similar to the within-task accuracy (p > 0.05). The MO–trained model achieved an accuracy of 60.82 % (MI test). On the other hand, combining two datasets yielded interesting results. ME and 50 % of the MI–trained model (50-shot) classified MI with a 69.21 % accuracy, which outperformed the within-task accuracy (p < 0.05), and MO and 50 % of the MI–trained model showed an accuracy of 66.75 %. Of the low performers with a within-task accuracy of 70 % or less, 90 % (n = 21) of the subjects improved in training with ME, and 76.2 % (n = 16) improved in training with MO on the MI test at 50-shot. These results demonstrate that task-to-task transfer learning is possible and could be a promising approach to building a user-friendly training protocol in MI-BCI.},

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tppubtype = {article}

}

@article{harel2024task,

title = {Task-guided attention increases non-linearity of steady-state visually evoked potentials},

author = {Asaf Harel and Oren Shriki},

doi = {https://doi.org/10.1088/1741-2552/ad8032},

year  = {2024},

date = {2024-09-26},

urldate = {2024-01-01},

journal = {Journal of Neural Engineering},

abstract = {Attention is a multifaceted cognitive process, with nonlinear dynamics playing a crucial role. In this study, we investigated the involvement of nonlinear processes in top-down visual attention by employing a contrast-modulated sequence of letters and numerals, encircled by a consistently flickering white square on a black background - a setup that generated steady-state visually evoked potentials. Nonlinear processes are recognized for eliciting and modulating the harmonics of constant frequencies. We examined the fundamental and harmonic frequencies of each stimulus to evaluate the underlying nonlinear dynamics during stimulus processing. In line with prior research, our findings indicate that the power spectrum density of EEG responses is influenced by both task presence and stimulus contrast. By utilizing the Rhythmic Entrainment Source Separation (RESS) technique, we discovered that actively searching for a target within a letter stream heightened the amplitude of the fundamental frequency and harmonics related to the background flickering stimulus. While the fundamental frequency amplitude remained unaffected by stimulus contrast, a lower contrast led to an increase in the second harmonic's amplitude. We assessed the relationship between the contrast response function and the nonlinear-based harmonic responses. Our findings contribute to a more nuanced understanding of the nonlinear processes impacting top-down visual attention while also providing insights into optimizing brain-computer interfaces.},

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pubstate = {published},

tppubtype = {article}

}

@article{cha2024effect,

title = {Effect of Electroencephalography-based Motor Imagery Neurofeedback on Mu Suppression During Motor Attempt in Patients with Stroke},

author = {Seungwoo Cha and Kyoung Tae Kim and Won Kee Chang and Nam-Jong Paik and Ji Soo Choi and Hyunmi Lim and Won-Seok Kim and Jeonghun Ku},

doi = {https://doi.org/10.21203/rs.3.rs-5106561/v1},

year  = {2024},

date = {2024-09-26},

urldate = {2024-01-01},

journal = {Journal of NeuroEngineering and Rehabilitation },

abstract = {Objective

The primary aims of this study were to explore the neurophysiological effects of motor imagery neurofeedback using electroencephalography (EEG), specifically focusing on mu suppression during serial motor attempts and assessing its potential benefits in patients with subacute stroke.



Methods

A total of 15 patients with hemiplegia following subacute ischemic stroke were prospectively enrolled in this randomized cross-over study. This study comprised two experiments: neurofeedback and sham. Each experiment included four blocks: three blocks of resting, grasp, resting, and intervention, followed by one block of resting and grasp. During the resting sessions, the participants fixated on a white cross on a black background for 2 minutes without moving their upper extremities. In the grasp sessions, the participants were instructed to grasp and release their paretic hand at a frequency of about 1 Hz for 3 minutes while fixating on the same white cross. During the intervention sessions, neurofeedback involved presenting a punching image with the affected upper limb corresponding to the mu suppression induced by imagined movement, while the sham involved mu suppression of other randomly selected participants 3 minutes. EEG data were recorded during the experiment, and data from C3/C4 and P3/P4 were used for analyses to compare the degree of mu suppression between the neurofeedback and sham conditions.



Results

Significant mu suppression was observed in the bilateral motor and parietal cortices during the neurofeedback intervention compared with the sham condition across serial sessions (p < 0.001). Following neurofeedback, the real grasping sessions showed progressive strengthening of mu suppression in the ipsilesional motor cortex and bilateral parietal cortices compared to those following sham (p < 0.05), an effect not observed in the contralesional motor cortex.



Conclusion

Motor imagery neurofeedback significantly enhances mu suppression in the ipsilesional motor and bilateral parietal cortices during motor attempts in patients with subacute stroke. These findings suggest that motor imagery neurofeedback could serve as a promising adjunctive therapy to enhance motor-related cortical activity and support motor rehabilitation in patients with stroke.},

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tppubtype = {article}

}

@article{jeong2024attentional,

title = {Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke},

author = {Chang Hyeon Jeong and Hyunmi Lim and Jiye Lee and Hye Sun Lee and Jeonghun Ku and Youn Joo Kang},

doi = {10.3389/fnins.2024.1373589},

year  = {2024},

date = {2024-03-18},

urldate = {2024-03-18},

journal = {Frontiers in Neuroscience},

volume = {18},

pages = {1373589},

publisher = {Frontiers},

abstract = {Introduction: Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity. Materials and methods: Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand.The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age.The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.},

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pubstate = {published},

tppubtype = {article}

}

@article{klee2024effect,

title = {The Effect of Jittered Stimulus Onset Interval on Electrophysiological Markers of Attention in a Brain–Computer Interface Rapid Serial Visual Presentation Paradigm},

author = {Daniel Klee and Tab Memmott and Barry Oken},

doi = {https://doi.org/10.3390/signals5010002},

year  = {2024},

date = {2024-01-09},

urldate = {2024-01-01},

journal = {Signals},

volume = {5},

number = {1},

pages = {18–39},

publisher = {MDPI},

abstract = {Brain responses to discrete stimuli are modulated when multiple stimuli are presented in sequence. These alterations are especially pronounced when the time course of an evoked response overlaps with responses to subsequent stimuli, such as in a rapid serial visual presentation (RSVP) paradigm used to control a brain–computer interface (BCI). The present study explored whether the measurement or classification of select brain responses during RSVP would improve through application of an established technique for dealing with overlapping stimulus presentations, known as irregular or “jittered” stimulus onset interval (SOI). EEG data were collected from 24 healthy adult participants across multiple rounds of RSVP calibration and copy phrase tasks with varying degrees of SOI jitter. Analyses measured three separate brain signals sensitive to attention: N200, P300, and occipitoparietal alpha attenuation. Presentation jitter visibly reduced intrusion of the SSVEP, but in general, it did not positively or negatively affect attention effects, classification, or system performance. Though it remains unclear whether stimulus overlap is detrimental to BCI performance overall, the present study demonstrates that single-trial classification approaches may be resilient to rhythmic intrusions like SSVEP that appear in the averaged EEG.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{demarest2023novel,

title = {A Novel Theta-Controlled Vibrotactile Brain-Computer Interface To Treat Chronic Pain: A Pilot Study},

author = {Phillip Demarest and Nabi Rustamov and James Swift and Tao Xie and Markus Adamek and Hohyun Cho and Elizabeth Wilson and Zhuangyu Han and Alexander Belsten and Nicholas Luczak and others},

doi = {https://doi.org/10.21203/rs.3.rs-2973437/v1},

year  = {2023},

date = {2023-06-01},

urldate = {2023-01-01},

abstract = {Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4–7 Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study's objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity and pain interference scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.



},

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pubstate = {published},

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}

@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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pubstate = {published},

tppubtype = {article}

}

@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.},

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pubstate = {published},

tppubtype = {article}

}