EEG-based Brain-Computer Interfaces (BCI) is a non-invasive technique used to translate brain activity to commands that control an effector (such as a computer keyboard, mouse, etc). Many patients who cannot communicate effectively, such as those who have suffered from a stroke, locked-in syndrome, or other neurodegenerative diseases, rely on BCI’s to stay connected. A few of the most common types of BCI’s modalities are P300, SSVEP, slow cortical potentials, and sensorimotor rhythms. With Wearable Sensing’s revolutionary dry EEG technology, nearly any type of BCI is possible with our research-grade signal quality. Since DSI systems are extremely easy to use and comfortable, this has opened the door to translating a wide range of BCI applications to the real- and virtual- worlds.
P300, otherwise known as the oddball paradigm, is an event-related potential (ERP) in which the brain elicits a unique response roughly 300ms after an “odd” stimulus is presented. This response can be decoded and classified in real-time for a variety of different applications.
One such use case is known as a P300 speller, in which a series of letters are flashed on a screen, and when the “target” letter pops up, our brain has the P300 response, which can then be transformed into a letter selection.
Betts Peters, Dr. Melanie Fried-Oken, and their team at Oregon Health & Science University have developed a P300 speller using the DSI-24, and have validated its functionality on subjects with Locked-In syndrome.
Steady State Visually Evoked Potentials (SSVEP) are natural responses to visual stimuli at specific frequencies. In a typical SSVEP paradigm, targets will flash at differing frequencies, anywhere from 3.5 Hz – 75 Hz, and depending on which target the subject is attending to, the brain will have a characterizable response at such specific frequency.
As shown in the video, a 12 target numbered keyboard is setup, and the subject is counting up. There is no training required, and the algorithm can correctly classify in under 1 second, in some cases.
This specific SSVEP software was developed by Wearable Sensing’s collaborater in China, Neuracle, and is available for purchase for all DSI systems. The software comes ready to use, with customizable 12-count and 40-count keyboards designed for ultra-rapid, high-accuracy classification.
Motor Imagery is a BCI technique in which the subject imagines performing a movement with a particular limb. This then alters the rhythmic activity in locations in the sensorimotor cortex that correspond to the imagined limb. The BCI can decode these signals, and translate the imagined movement into feedback in the form of cursor movements or other computer commands.
The DSI-24 was featured at an interactive art installation “Mental Work” at the Ecole Polytechnique Federale de Lausanne (EPFL) Switzerland. During the exhibit, subjects were presented with a wheel that was controlled by the subject thinking about moving either one of their arms.
Neurolutions is a medical device company developing neuro-rehabilitation solutions that seek to restore function to patients who are disabled as a result of neurological injury. The Neurolutions IpsiHand system provides upper extremity rehabilitation for chronic stroke patients leveraging brain-computer interface and advanced wearable robotics technology.
By utilizing the DSI-7, Neurolutions is able to use Motor Imagery techniques to decode a patients intent to move their finger, which then instructs the exoskeleton to physically move the finger. With repeated sessions, patients can regain control of their lost limbs.
What fires together, wires together!
Harel, Asaf; Shriki, Oren
Task-guided attention increases non-linearity of steady-state visually evoked potentials Journal Article
In: Journal of Neural Engineering, 2024.
@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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Cha, Seungwoo; Kim, Kyoung Tae; Chang, Won Kee; Paik, Nam-Jong; Choi, Ji Soo; Lim, Hyunmi; Kim, Won-Seok; Ku, Jeonghun
Effect of Electroencephalography-based Motor Imagery Neurofeedback on Mu Suppression During Motor Attempt in Patients with Stroke Journal Article
In: Journal of NeuroEngineering and Rehabilitation , 2024.
@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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pubstate = {published},
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}
Xu, Jihong; Chen, Tianran; Yan, Lirong
Improvement Of An Untrained Brain-computer Interface System Combined With Target Recognition Proceedings Article
In: 2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–6, IEEE 2024.
@inproceedings{xu2024improvement,
title = {Improvement Of An Untrained Brain-computer Interface System Combined With Target Recognition},
author = {Jihong Xu and Tianran Chen and Lirong Yan},
doi = {10.1109/ECAI61503.2024.10607572},
year = {2024},
date = {2024-07-30},
urldate = {2024-01-01},
booktitle = {2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)},
pages = {1–6},
organization = {IEEE},
abstract = {In the current commonly used Steady State Visual Evoked Potential (SSVEP) paradigm, the stimuli are mostly white flashing blocks superimposed on a black background, which is monotonous and easy to cause subject fatigue with prolonged flashing stimuli. The stimulus paradigm is mostly divorced from the actual control environment, and lacks a direct connection with the control task. The mainstream classification algorithms usually analyze the data with a fixed window length, which is lack of generalizability to different subjects, and the classification performance index needs to be further improved. In this study, the SSVEP stimulus paradigm was improved by combining the YOLOv5 algorithm, which changed from the traditional black background to the actual control environment. It superimposed SSVEP stimulus blocks of different frequencies at each recognized target location. The stimulus paradigm was not stripped from the control scene, and the Filter Bank Criterion Correlation Analysis (FBCCA) algorithm was chosen to analyze it. The FBCCA algorithm was further improved by using a dynamic window strategy, which automatically adjusts the window length of each experiment according to the characteristics of each subject. This improves the versatility of the algorithm and increases the recognition accuracy and Information Transfer Rate (ITR). After the improvement, the offline experimental data were analyzed. The improved algorithm achieved an average accuracy of 87.08%, which was 17.29% higher than the original algorithm. Additionally, the average ITR was 74.28 bits/min, which was 36.51 bits/min higher than the original algorithm.},
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pubstate = {published},
tppubtype = {inproceedings}
}
Jeong, Chang Hyeon; Lim, Hyunmi; Lee, Jiye; Lee, Hye Sun; Ku, Jeonghun; Kang, Youn Joo
In: Frontiers in Neuroscience, vol. 18, pp. 1373589, 2024.
@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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Klee, Daniel; Memmott, Tab; Oken, Barry
In: Signals, vol. 5, no. 1, pp. 18–39, 2024.
@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}
}
Ferrisi, Leonardo M
Optimizing an assistive Brain Computer Interface that uses Auditory Attention as Input Masters Thesis
2023.
@mastersthesis{ferrisi2023optimizing,
title = {Optimizing an assistive Brain Computer Interface that uses Auditory Attention as Input},
author = {Leonardo M Ferrisi},
url = {https://digitalworks.union.edu/cgi/viewcontent.cgi?article=3754&context=theses},
year = {2023},
date = {2023-06-01},
urldate = {2023-01-01},
abstract = {Brain Computer Interfaces (BCIs) allow individuals to operate technology using (typically consciously controllable) aspects of their brain activity. Auditory BCIs utilize principles of Auditory Event Related
Potentials or Auditory Evoked Potentials as a reproducible controllable features that individuals can use to operate a BCI. These Auditory BCIs in their most basic format can allow users to answer yes or no questions by listening to either one auditory stimuli or the other. Current accuracy in intended response detection for these kinds of BCIs can be as good as mean accuracy of 77 % [5]. BCI research tends to optimize the computer side of the system however the ease of use for the human operating the system is an important point of consideration as well. This research project aimed to determine what factors make a human operator able to achieve the highest accuracy using a given previously successfully demonstrated classifier. This research project primarily sought to answer the questions; to what degree people can improve their accuracy in operating an Auditory BCI and what factors of the stimulus used can be altered to achieve this. The results of this project, obtained through the data collected from six individuals, found that slower stimuli speeds for eliciting Auditory Event Related Potentials were significantly better at achieving higher prediction accuracies compared to faster stimulus speeds. The amount of time spent using the system appeared to result in diminishing returns in accuracy regardless of condition however not before an initial spike in greater classifier prediction accuracy for the second condition run on each subject. Although further research is needed to gain more conclusive evidence for or against the hypothesis, the results of this research may be able suggest that individuals can improve their performance using Auditory BCIs with practice at optimal parameters albeit within a given time frame before experiencing diminishing returns. These findings would stand to provide benefit both to continued research in making optimal non-invasive alternative communication technologies as well as making progress in finding the potential ceiling in accuracy that an Auditory BCI can have in interpreting brain activity for the intended action of the user},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Demarest, Phillip; Rustamov, Nabi; Swift, James; Xie, Tao; Adamek, Markus; Cho, Hohyun; Wilson, Elizabeth; Han, Zhuangyu; Belsten, Alexander; Luczak, Nicholas; others,
A Novel Theta-Controlled Vibrotactile Brain-Computer Interface To Treat Chronic Pain: A Pilot Study Journal Article
In: 2023.
@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.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kambhamettu, Sudhendra; Cruz, Meenalosini Vimal; Anitha, S; Chakkaravarthy, S Sibi; Kumar, K Nandeesh
Brain-Computer Interface-Assisted Automated Wheelchair Control Management--Cerebro: A BCI Application Journal Article
In: Brain-Computer Interface: Using Deep Learning Applications, pp. 205–229, 2023.
@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.
},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Won, Kyungho; Kim, Heegyu; Gwon, Daeun; Ahn, Minkyu; Nam, Chang S; Jun, Sung Chan
Can Vibrotactile Stimulation and tDCS Help Inefficient BCI Users? Journal Article
In: 2022.
@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}
}
Humphries, Joseph B; Mattos, Daniela JS; Rutlin, Jerrel; Daniel, Andy GS; Rybczynski, Kathleen; Notestine, Theresa; Shimony, Joshua S; Burton, Harold; Carter, Alexandre; Leuthardt, Eric C
Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface Journal Article
In: Brain-Computer Interfaces, vol. 9, no. 3, pp. 179–192, 2022.
@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}
}
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