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
  2. Cerebrum, Cerebellum, brain stem
  3. Right brain, left brain
  4. Lobes of brain
  5. Deep structures
  6. Cranial nerves
  7. Blood supply
  8. Language, memory
  9. Cells of the brain
  10. Neurons and neuroscience
  11. Neurons – dentrites, soma, axon. Axon terminal, synaptic gap
  12. Resting potential, threshold, action potential
  13. Neurotransmitters
  14. Nerves and neurons
  15. Nervous system - peripheral and central nervous system
  16. Cognitive neuroscience
  17. Brain dynamics
  18. Brain waves
  19. Alpha, Beta, Theta and Delta Waves; mu waves
  20. Meditation, relaxation, listening to music (Albert Einstein’s story)
  21. EEG, EOG , EMG
  22. Traumatic Brain Injury (TBI)
  23. Nanomaterials and nanostructures
  24. Carbon nanotubes
  25. Magnetic nanotubes
  26. Gold and other biocompatible nanowires
  27. Nanosensors and microelectrode arrays
  28. Nanosenor electrodes- invasive and non-invasive
  29. Wet electrodes vs dry electrodes
  30. Microelectrode array (MEA)
  31. Intracranial pressure sensors
  32. Synthesis and fabrication in clean rooms
  33. Flexible bioelectronics and thin film transistors
  34. Flexible thin film transistor
  35. Amplifiers, microprocessors
  36. Blue tooth, WiFi module
  37. RFID
  38. Integration of nanosensors and electronics
  39. Smart textile cap, hats
  40. Conventional wet electrodes
  41. Nanowire based dry electrodes
  42. Wireless nanoengineering systems for brain controlled activities
  43. Smart textile caps, hats
  44. Wireless EEG, EOG measurements
  45. Software
  46. Robots and robotic engineering
  47. Neuroengineering
  48. EEG neurofeedback
  49. Computer software
  50. Neuro-robotic system
  51. Brain Machine/Computer Interface
  52. Animal experiment (monkey feeding by robotic arm)
  53. Implantable nano and neuro devices controlling computer, robots, machine, etc
  54. Applications
  55. Sleep disorders and sleep apnea; monitoring and control of grinding teeth during sleep (dental therapy)
  56. Monitoring and control of movement disorders; Parkinson’s disease and others
  57. Monitoring Alzheimer’s disease
  58. Epilepsy and seizure monitoring and control
  59. Thoughts controlled robots, computer and other examples
  60. Diagnostic and therapeutic techniques for TBI including the soldiers in the battle field, high altitude, etc
  61. Movies on selected surgeries