Non-invasive BCI-controlled Assistive Devices
Study Details
Study Description
Brief Summary
A brain-computer interface (BCI) decodes users' behavioral intentions or mental states directly from their brain activity, thus allowing operation of devices without requiring any overt motor action. One major modality for BCI control is based on motor imagery (MI), which is the mental rehearsal of the kinesthetics of a movement without actually performing it. MI-based BCIs translate motor intents into control commands for external devices. A major challenge in such BCIs is differentiating MI patterns corresponding to fine hand movements of the same limb from non-invasive EEG recordings with low spatial resolution since the cortical sources responsible for these movements are overlapping. In this study, the investigators hypothesize that neuromuscular electrical stimulation (NMES) applied contingent to the voluntary activation of the primary motor cortex through MI can help differentiate patterns of activity associated with different hand movements of the same limb by consistently recruiting the separate neural pathways associated with each of the movements within a closed-loop BCI setup. This is expected to be associated with neuroplastic changes at the cortical or corticospinal levels.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: NMES-BCI Sensory-threshold electrical stimulation is delivered to the flexors/extensors of the forearm contingent to the voluntary activation of the motor cortex by motor imagery of hand flexion/extension as detected by a closed-loop BCI. |
Device: NMES-BCI
Electroenceauphalography (EEG) signals will be recorded from subjects as they perform cued tasks for flexing/extending their non-dominant hand. The signals will be processed and classified in real-time using machine learning algorithms to trigger electrical stimulation on the flexors/extensors of the targeted arm contingent to the detection of a subject-specific flexion/extension EEG patterns.
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| Active Comparator: Visual-BCI Bar-based visual feedback is provided on a screen contingent to the voluntary activation of the motor cortex by motor imagery of hand flexion/extension as detected by a closed-loop BCI. |
Device: Visual-BCI
Electroenceauphalography (EEG) signals will be recorded from subjects as they perform cued tasks for flexing/extending their non-dominant hand. The signals will be processed and classified in real-time using machine learning algorithms to control the right/left movement of a bar on a computer screen. The bar feedback is contingent to the detection of a subject-specific flexion/extension EEG patterns.
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Outcome Measures
Primary Outcome Measures
- Change in the BCI command delivery accuracy [Difference between the week before versus after each intervention]
The command delivery accuracy reflects the level of control of the subject when using the BCI. It measures the percentage of trials in which the subject-specific classifier that is used to differentiate the different imagined movements could accumulate enough evidence to support the presence of EEG patterns specifically associated with the imagined movement in those trials. The score is 0-100, and the higher the value, the better the outcome.
- Change in fMRI activation for different imagined movements [Difference between the week before versus after each intervention]
The clusters of significant activation during MI of different movements would be more separable The activation associated with different MI tasks would be more discriminable
Secondary Outcome Measures
- Stability and separability of Motor Imagery features [Difference between the week before versus after each intervention]
The features corresponding to different motor imagery tasks become more separable and are more stable at the end of the intervention.
- Changes in motor-evoked potential amplitude [Difference between the week before versus after each intervention]
Continuous measure, the higher the better
- Changes in electroencephalography functional connectivity [Difference between the week before versus after each intervention]
Continuous measure, the more significant changes the better
Eligibility Criteria
Criteria
Inclusion Criteria:
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Able-bodied participants:
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good general health
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normal or corrected vision
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no history of neurological/psychiatric disease
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ability to read and understand English (Research Personnel do not speak Spanish)
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Subjects with motor disabilities
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motor deficits due to: unilateral and bilateral stroke / spinal cord injury / motor neuron diseases (i.e. amyotrophic lateral sclerosis, spino-cerebellar ataxia, multiple sclerosis) / muscular diseases (i.e. myopathy) / traumatic or neurological pain / movement disorders (i.e. cerebral palsy) / orthopedic / traumatic brain injury / brain tumors
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normal or corrected vision
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ability to read and understand English (Research Personnel do not speak Spanish)
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ability to provide informed consent
Exclusion Criteria:
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Subjects with motor disabilities
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short attentional spans or cognitive deficits that prevent to remain concentrated during the whole experimental session
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heavy medication affecting the central nervous system (including vigilance)
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concomitant serious illness (e.g., metabolic disorders)
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All participants
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factors hindering EEG/EMG acquisition and FES/tdCS/tACS delivery (e.g., skin infection, wounds, dermatitis, metal implants under electrodes)
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criteria identified in safety guidelines for MRI and TMS, in particular metallic implants
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | The University of Texas at Austin | Austin | Texas | United States | 78712 |
Sponsors and Collaborators
- University of Texas at Austin
Investigators
- Principal Investigator: Jose del R. Millan, PhD, The University of Texas at Austin
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 2020030073