Analysis of Brain Signal Generation for EEG

Abstract

• The overall goal of the WUSTL-BCI project is to have a brain-controlled interface (BCI) by means of a non-invasive electroencephalogram (EEG). The interface would then be used to control a hand exoskeleton for aiding in the rehabilitation of hand motor skills in people who suffer from severe motor impairment. However, the scope of BCIs is not limited to controlling the hand exoskeleton. It can be applied to just about any system that a person could otherwise control—personal computers, cars, and cell phones, just to scratch the surface. The 2D Control group focuses on identifying two sufficiently independent brain signals that can be used to control different processes, as in moving a mouse along a line and being able to click, or two parts of the same process, as in moving the mouse anywhere along a two dimensional plane. Applied to this project, 2D control would mean a second dimension of freedom for the motion of the hand exoskeleton.

Goals

• The current goal for the 2D-Control project is identifying the types of physical movements that generate the best signals for the EEG. These brain signals are the basis for the project, and without a functional understanding of how they work and how to generate a clean, consistent, and coherent signal, progress for any of the sub-projects would be extremely difficult.

Equipment and Program

• For our experiments, a specialized EEG headset (Emotiv® EPOC Neuroheadset) is used. To transport and to analyze EEG signal data into the computer, Emotiv® Control Panel, BCI 2000® Cursor Task, BCI 2000® StimPresentation, and BCI 2000® Offline Analysis were used.
  • For a more indepth view at our equipment setup go [here]

Experimental Design and Hypotheses

Experiment 1 (Cursor Task)

• This is the first experiment Tom and DoHyun Kim performed. Without deep understanding of EEG signals, we chose several movements (Shoulder Rotation, Ankle Tapping, and Wrist Twisting). This was more likely EEG signal training period for both of us.

Experiment 2 (LR StimPresentation)

• The first LR StimPresentation experiment was for understanding general EEG signal properties of body movements. We performed EEG data collections for Finger Movements, Bicep Carlings (with different velocity & force generated), Tongue Movements, Bicep Lifting, Fist Squeezing (both for passive and active force), Wrist Twisting, Wrist Holding, Wrist Rotation with Open Hand, and Wrist Rotation with Closed Fist. These series of EEG data collection gives us insight of possible hypothesis of our experiments.

Experiment 3 (Constant Action vs. Repetitive Action)

• This experiment was designed for determining whether Constant Holding will generate stronger R2 value than Repetitive Action for specific motion. Connecting Left hand Fingers and Wrist Twisting were performed for this test.

Experiment 4 (Few Large Muscles vs. Many Small Muscles)

• This experiment was designed for determining whether EEG signals for Few Large Muscles will generate stronger R2 value than EEG signals for Many Small Muscles. Tom performed a StimPresentation test for comparing his EEG signals of Quad Muscle Contraction and Fist Squeezing.

Experiment 5 (Imaginary Action vs. Actual Action)

• This experiment is designed to explore the possibilities of using imaginary action generated EEG signals in real-time data acquisition. EEG data for Actual & Imaginary Bicep Curling and Fist Squeezing, EEG signal data for Imaginary 2D Ball Control, and EEG signal data for Visually Stimulated Imaginations were collected.

Experiment 6 (Eye Movements// it’s more like EMG artifacts)

• This experiment was more likely an artifact test for Eye Movements. 2D Slow Eye Movements and Saccade Movements were performed for collecting EMG/EEG signals.

Experiment 7 (Trained Muscle Movement vs. Untrained Muscle Movement)

• There were significant differences in EEG data R2 values for Left vs. Right movements in Experiment 2. We made a hypothesis that motions for undeveloped side of brain or muscles will generate higher EEG data R2 values. In this experiment, we wrote specific letter (it was a mirror-imaged Korean word that Tom and I never wrote before) for collecting EEG signal data.

Experiment 8 (Directional Movements)

• reference: [1]

• From the Quantitative Physiology class, BME 301A, DoHyun Kim learned about directional properties of the ECoG signals inM1 sites. We tested arm movements with different directionality to confirm whether EEG signals also have directional properties for movements.