My seventh semster B-tech seminar. on Brain Computer Interface.

1. BRAIN COMPUTER INTERFACE

2. INTRODUCTION Direct communication pathway between a brain and an external device. Provides a means for communication between the human brain and a computer, so the information in the neural activities in the brain can be exchanged with the computer. Thus BCI extracts electro-physical signals from suitable components of the brain and process them to generate control signals for computers, robotic machines or communication devices.

3. Make it possible to get a work done by just thinking about it. Might help us better understand how the human brain works in terms of reorganization, learning, memory, attention, thinking, social interaction, motivation, interconnectivity, and much more. Allows us to develop a new class of bioengineering control devices and robots to provide daily life assistance to handicapped and elderly people. Several potential applications of BCI hold promise for rehabilitation and improving performance, such as treating emotional disorders (for example, depression or anxiety), easing chronic pain, and overcoming movement disabilities due to stroke. Expands possibilities for advanced human computer interfaces (HCIs), making them more natural, flexible, efficient, secure, and user-friendly by enhancing the interaction between the brain, the eyes, the body, and a robot or a computer. Why BCI???

4. BUILDING BLOCKS OF BCI

5. Challenging Areas in this field are Neural Signal Behavior Neuron Modeling Neurons in BRAIN

6. Neurons Undergo continuous interactions in brain. Thousands of them connected in infinite series and branching. They have space b/w them ‘synapse’, where signal is exchanged. Generate electrical impulses (spikes) due to a phenomenon called action potential. Each ‘Spike train’ has distinct characteristics. Due to heavy crowd, spike train interfere and form ensemble from which identifying components become tough. Technique for classifying these spikes based on their origin is called SPIKE SORTING. Neural Signals

7. Neural signals are essentially pulse streams. Over hundred billion neurons in brain, making their identification and understanding a daunting task. Difficult to associate a signal with its actual neural source. Signals characterized and classified according by pattern recognition and adaptive signal processing techniques etc. Techniques to readout brain are Electroencephalography (EEG) Magnetoencephalography (MEG) ElectroCortigography (ECoG) Neuron Signal Behavior

8. Invasive Techniques: Probes are directly implanted in the brains. Produce the highest quality signals of BCI devices Are prone to scar tissue build-up, causing the signal to become weaker or even lost as the body reacts to a foreign object in the brain. Tissue friendly probes required. Non Invasive technique: Probes fixed in outer skull. in which medical scanning devices or sensors mounted on caps or headbands read brain signals. Very sensitive probes required. Signal Extractor

9. EEG An electroencephalogram is a measure of the brain's voltage fluctuations as detected from scalp electrodes. It is an approximation of the cumulative electrical activity of neurons. Most common, but less generalization. Non-Invasive technique. MEG Non invasive technique. More generalization achieved. ECoG Higher spatial resolution, better signal-to-noise ratio, wider frequency range, and lesser training requirements than scalp-recorded EEG, and at the same time has lower technical difficulty, lower clinical risk, and probably superior long-term stability than intracortical single-neuron recording.

10. Delta <4 Hz Everywhere occur during sleep, coma Theta 4-7 Hz temporal and parietal correlated with emotional stress (frustration & disappointment) Alpha 8-12 Hz occipital and parietal reduce amplitude with sensory stimulation or mental imagery opathy Beta 12-36 Hz parietal and frontal can increase amplitude during intense mental activity Mu 9-11 Hz frontal (motor cortex) diminishes with movement or intention of movement Lambda sharp, jagged occipital correlated with visual attention Vertex higher incidence in patients with epilepsy. EEG Signal Classification

11. Concentrated on developing behavioral models of neurons. Mathematical models of neurons are developed keeping their utility in mind. Electrical analogues of biological parameters are described. Neuron Modeling

12. High Sensitivity and High Selectivity. Reliability Biocompatibility Chemical reaction may corrode the material inside brain. Mismatch in neuron and probe size may damage the nerve cell during penetration. Bio-inert materials to be converted to bio-mimetic material. Probes

13. Signal extractor collects electrical signal from neural probes and processes them to extract signals to be used. Neural Processor maybe an ASIC or a DSP tuned for neuro-computing. The processor has to process the neural signals to classify, characterize and identify them. Large number of inputs and outputs and a highly parallelized architecture. High traffic necessitates compression and thus on-chip implementation of the compression algorithm. Neural Processors

14. Classification is the main aspect first. Neural signals need to be associated to particular physiological phenomena. Signal processing necessitates development of encoding of neural data for lesser bandwidth. Thus reducing chip-size and increasing speed of processor. Neural Signal Processing

15. Applications

16. Intelligent Prosthesis Surrogate human body parts to restore or enhance functionality of that organ. Final recipient of neural signal information to perform any operation. Includes Prosthetic limbs like legs, arms etc, bionic eyes. Devices categorized according to functionality i.e. whether they augment muscular action or nervous. BCI brings them to life by bringing them in synchronization with mind.

17. Mind Control Controlling devices by just thinking. Ability to read other’s mind by studying signal pattern. (Although a long way to go) Advanced gaming option. Thus creation of virtual world. Once the storing mechanism of brain is understood, we may be able to download skills the “The Matrix” style. With Brain completely duped, humans may become immortal.

18. Poses many ethical problems Need to unravel ambiguities in human emotions such as ethics, good, bad etc to develop intelligent and thinking machines. Case of deadlock arises when dealing with multiple request of equal priorities. EPILOGUE

19. Suprio Bhattacharya eds. On ‘Brain- Computer Interface” , Electronics For You magazine, July.2010. Wiki.net “Brain Computer Interface” http//www.biocontrol.com/eeg.html Cromwell and Pfeiffer, Biomedical Instrumentation, vol.2 References