Brain Computer Interface (BCI) Technology for Stroke Hand Rehabilitation
Condition: Stroke
MeSH terms: Stroke
Study type: Interventional

Summary: 
      This study is carried out to find out if Brain Computer Interface (BCI) technology or BCI
      technology coupled with robotic technology using a Haptic Knob will benefit patients with arm
      paralysis after stroke. BCI uses EEG-based motor imagery to detect user's thinking abilities
      which control motor movement. Haptic Knob is a novel robotic device, which specifically
      trains the wrist and hand with intensive repetitions in a supported environment.
    

Detailed description: 
      Physical therapy approaches are the de facto rehabilitation for stroke, which involve human
      therapists to assist stroke patients in recovering their motor ability. Modern rehabilitation
      technologies include robotics, functional electrical stimulation, transcranial magnetic
      stimulation and virtual reality. Robotic rehabilitation alleviates the labor-intensive
      aspects of physical rehabilitation by human therapists and could potentially improve the
      productivity of stroke rehabilitation. However, it is fundamentally based on movement
      repetition with visual feedback that helps stroke patients improve motor ability in their
      weak stroke-affected arms and legs. However, the robot is still able to move the weak part of
      the patient even if the patient is not attentive towards the training and thus the robotic
      training becomes a passive activity. In contrast, BCI-based robotic training works by
      ensuring active engagement by the hemiparetic patients in making a volitional movement. In
      addition, hemiplegic or locked-in stroke patients who do not have any motor power on the
      affected limbs are then able to engage and perform a volitional movement on these affected
      limbs.

      BCI-based robotic rehabilitation fills this gap by detecting the motor intent of hemiplegic
      patients from the Electroencephalogram (EEG) signals to drive the robotic rehabilitation.
      This BCI-based robotic rehabilitation for stroke research project was jointly conducted by
      Tan Tock Seng Hospital (TTSH), National Neuroscience Institute (NNI) and Institute for
      Infocomm Research (I2R). Preliminary clinical trials performed at TTSH have shown that stroke
      patients can operate the BCI as effective as healthy subjects.

      Specifically, this research project will address the following gaps in the area of
      rehabilitation for stroke:

        1. Single-modal BCI - The current system employs a single modal non-invasive EEG-based BCI
           that detects motor intent using at least 2.5 seconds of EEG data. Hence, the research of
           an advanced multi-modal BCI such as synergizing near-infrared spectroscopy with EEG to
           yield a more responsive and effective BCI-based robotic rehabilitation system is
           proposed.

        2. Standard therapy - The current system employs a standard therapy for all the stroke
           patients. However, physiotherapists and occupational therapists usually adopt a more
           individualized therapy for each stroke patients. Hence, research on an individualized
           therapy for each stroke patient according to his or her learning rate and neurological
           insult is proposed.

        3. Only physiological rehabilitation - The current system only performs physiological
           rehabilitation of motor functions of stroke patients. Currently some validated scales
           for post-stroke depression such as Beck depression inventory, CES-D, Zung scale, State
           trait, HADS etc are difficult to administer in stroke patients who cannot participate
           with assessment due to impaired language or cognitive abilities. Hence an advanced
           BCI-based rehabilitation system that also detects the mental state of the stroke patient
           is proposed to cover both physiological and psychological rehabilitation.

        4. Upper Limb rehabilitation - The current system which uses the clinically-proven MIT
           Manus robotic rehabilitation system, only performs upper limb rehabilitation for stroke
           patients in gross reach patterns. Human hand skills, in contrast, consist of more
           complex manipulation movement patterns which can be intervened by BCI-based robotic
           rehabilitation. Hence, an advanced BCI-based rehabilitation system that covers the hand
           function is proposed to cover the rehabilitation of the entire upper extremity.
    

Intervention name(s): Occupational Therapy; BCI Haptic Knob; Haptic Knob
Intervention type(s): Other; Device; Device
Intervention description(s): Use of conventional manual facilitation and function-based training used in conventional occupational therapy training for post-stroke upper limb weakness.
Training is modelled along the neurodevelopmental techniques and will include stretching, tone management, weight bearing exercises, movement facilitation, selfcare training, arm ergometry by arm bicycles and grip strength training.
Training intensity is 1.5 hours for 3 times a week for 6 weeks consecutively.; BCI based robotic rehabilitation works by detecting the motor intent of the user from electroencephalogram signals to drive the robotic rehabilitation via Haptic Knob. Training intensity is 1.5 hours for 3 times a week for 6 weeks consecutively.; Haptic Knob is an upper limb robot designed for use in robotic-assisted rehabilitation of the stroke wrist and hand. Training intensity is 1.5 hours for 3 times a week for 6 weeks consecutively.
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: Single (Outcomes Assessor)
Primary purpose: Other
Keywords: Stroke Rehabilitation; Brain Computer Interface; Upper Limb Rehabilitation; EEG-based Motor Imagery; Robotic Rehabilitation; Haptic Knob