Tutorial/Workshop
Brain Engineering and Brain Machine Interface
Vijay K. Varadan, PhD, MD
Pennsylvania State University and University of Arkansas
A brain machine interface (BMI) or Brain Computer Interface (BCI) is a communication system that translates human's thought into signals to control devices such as a computer application or a neuroprosthesis. A BMI enables the brain to communicate with the external world by deciphering the brain activity. Hence, the assistive devices or systems using a BMI improve disabled people's quality of life. In addition, a BMI has been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, firefighting etc. Earlier researches demonstrate the feasibility of BMI with the invasive method by implanting the intracranial electrodes in the motor cortex of monkeys. While an invasive BMI can use good quality of brain signals, it is expensive and the implanting surgery may lead to undesirable side effects. A noninvasive BMI using electroencephalogram (EEG) signals are preferable for humans. EEG signals represent the electrical activity of millions of neurons in the brain. EEG has various properties and it can be used as a basis for a BMI: rhythmic brain activity, event-related potentials (EPRs), event-related desynchronization (ERD) and event-related synchronization (ERS). Different rhythmic brain activities will be shown depending on the level of consciousness. The brain waves are classified according to the frequency band: Delta (0.1-3 Hz), Theta (4-7 Hz), Alpha (8-12 Hz), Beta (12-30Hz) and Gamma (30-100Hz). These rhythms are affected by different actions and thoughts, for example the thinking of movement attenuates or changes a typical brain rhythm. The fact that the thoughts affect the brain rhythms connote the rhythmic brain activities can be used for the BCI. ERP represents the potential changes in EEG that occur in response to a particular event or a stimulus. ERD and ERS is the change of signal's power occurring in a given band, relative to a reference interval. Many researchers have been developing a BMI with two different approaches. The first is a pattern recognition approach which is based on cognitive mental tasks and the other is an operant conditioning approach based on the self-regulation of the EEG response.
The author’s group developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot. In addition, the correlation between brain and heart activity will be presented to illustrate emotion, stress level, attention deficiencies, autism, etc
Lecture will cover the following topics with selected videos in engineering and medicine:
1. Brain anatomy and functionality
a. Cerebrum, Cerebellum, brain stem
b. Right brain, left brain
c. Lobes of brain
d. Deep structures
e. Cranial nerves
f. Blood supply
g. Language, memory
h. Cells of the brain
2. Neurons and neuroscience
a. Neurons – dentrites, soma, axon. Axon terminal, synaptic gap
b. Resting potential, threshold, action potential
c. Neurotransmitters
d. Nerves and neurons
e. Nervous system - peripheral and central nervous system
f. Cognitive neuroscience
3. Brain dynamics
a. Brain waves
b. Alpha, Beta, Theta and Delta Waves; mu waves
c. Meditation, relaxation, listening to music (Albert Einstein’s story)
d. EEG, EOG , EMG
e. Traumatic Brain Injury (TBI)
4. Nanomaterials and nanostructures
a. Carbon nanotubes
b. Magnetic nanotubes
c. Gold and other biocompatible nanowires
5. Nanosensors and microelectrode arrays
a. Nanosenor electrodes- invasive and non-invasive
b. Wet electrodes vs dry electrodes
c. Microelectrode array (MEA)
d. Intracranial pressure sensors
e. Synthesis and fabrication in clean rooms
6. Flexible bioelectronics and thin film transistors
a. Flexible thin film transistor
b. Amplifiers, microprocessors
c. Blue tooth, WiFi module
d. RFID
7. Integration of nanosensors and electronics
a. Smart textile cap, hats
b. Conventional wet electrodes
c. Nanowire based dry electrodes
8. Wireless nano engineering systems for brain controlled activities
a. Smart textile caps, hats
b. Wireless EEG, EOG measurements
c. Software
9. Robots and robotic engineering
a. Neuroengineering
b. EEG neurofeedback
c. Computer software
d. Neuro-robotic system
10. Brain Machine/Computer Interface
a. Animal experiment (monkey feeding by robotic arm)
b. Implantable nano and neuro devices controlling computer, robots, machine, etc
11. Applications
a. Sleep disorders and sleep apnea; monitoring and control of grinding teeth during sleep (dental therapy)
b. Monitoring and control of movement disorders; Parkinson’s disease and others
c. Monitoring Alzheimer’s disease
d. Epilepsy and seizure monitoring and control
e. Thoughts controlled robots, computer and other examples
f. Diagnostic and therapeutic techniques for TBI including the soldiers in the battle field, high altitude, etc
g. Movies on selected surgeries