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!
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}
}
Kim, Min Gyu; Lim, Hyunmi; Lee, Hye Sun; Han, In Jun; Ku, Jeonghun; Kang, Youn Joo
In: Journal of Neural Engineering, vol. 19, no. 3, 2022.
@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}
}
Rustamov, Nabi; Humphries, Joseph; Carter, Alexandre; Leuthardt, Eric C
Theta-gamma coupling as a cortical biomarker of brain-computer interface mediated motor recovery in chronic stroke Journal Article
In: Brain Communications, vol. 4, iss. 3, 2022.
@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}
}
Klee, Daniel; Memmott, Tab; Smedemark-Margulies, Niklas; Celik, Basak; Erdogmus, Deniz; Oken, Barry S
Target-Related Alpha Attenuation in a Brain-Computer Interface Rapid Serial Visual Presentation Calibration Journal Article
In: Frontiers in Human Neuroscience, vol. 16, 2022.
@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}
}
Kim, Soram; Lee, Seungyun; Kang, Hyunsuk; Kim, Sion; Ahn, Minkyu
P300 Brain--Computer Interface-Based Drone Control in Virtual and Augmented Reality Journal Article
In: Sensors, vol. 21, no. 17, pp. 5765, 2021.
@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}
}
Lim, Hyunmi; Ku, Jeonghun
Superior Facilitation of an Action Observation Network by Congruent Character Movements in Brain--Computer Interface Action-Observation Games Journal Article
In: Cyberpsychology, Behavior, and Social Networking, vol. 24, no. 8, pp. 566–572, 2021.
@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}
}
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