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Real Time Intracranial EEG Experimental Interface

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Slide 1 :

Real Time Intracranial EEG Experimental Interface Abstract Electroencephalography (EEG) measures potential differences within the brain generated by the firing of neurons. The signal can be characterized by its frequency content into special frequency bands. It is hypothesized that certain frequency components of EEG correspond to heightened cognitive abilities. The 4-8 Hz frequency band (also known as the “theta” band) has been linked to greater memory performance. To test this hypothesis, a Brain Computer Interface (BCI) was created that used recorded EEG activity to drive a real time memory retrieval experiment. The current system was constructed as a platform for real time cognitive experimentation. An interface was created that allowed maximum control of experimental conditions. Two classifiers were used to characterize incoming EEG activity. The spectral classifier maximized the speed under which the experiment was conducted while the Neural Network Classifier used all available spatial EEG information. The classifiers were used to characterize the incoming EEG into two states: “theta” or “not theta”. Based on this binary classification, an experimental output was modified. In this case, the experimental output was the presentation of a word to the patient on a computer screen each time a “theta” event was discerned. The hypothesis is that words presented during high theta activity will be remembered by the patient at a greater rate than words presented randomly. Authors John Burke EE ‘07 Kylash Viswanathan EE ‘07 Ronald Williams EE ’07 Advisors Prof. Nabil Farhat Dr. Kareem Zaghloul Prof. Michael Kahana Special Thanks Joshua Jacobs, Neuralynx Inc. Demo Times: Thursday, April 19, 2007 System Overview: The Top-Level System overview is given below. EEG signals from the patient are recorded through the Neuralynx system after which they are processed by a C# program. Once processed, the signal is further processed in Matlab and fed into the classifier. The binary output of the classifier then controls the experimental output – a word is presented to the patient on a separate computer screen. System Flowchart: The system takes one of two pathways based on the mode of classification selected. Network Classifier: A three layer feed forward network was used for classification. The network Classifier went through two rounds of training before implementation within the system (block diagram lower left). First, back propagation altered the network weights and, second, the threshold for the network output was optimized by maximizing the d’ value of the classifier. Feature Selection PCA Error Back Propagation D prime maximization Training I Training II The d’ value maximized the hit rate of the classifier (correctly predicted theta events) and minimized the False positive rate (see figure above). The classifier output is given above. The red spikes are places where the output was above the threshold. A TTL pulse was sent at each of these points. Spectral Classifier: In contrast to the network classifier, the spectral algorithm used information from only one electrode to classify theta events. The classifier examined the Power Spectrum of the signal (see figure at left). If the ratio of the power in the 4-8 Hz band relative to the 2-4 Hz and 8-10 Hz bands exceeded a given threshold, the activity was classified as a positive theta event (see figure at right). System Interface and Summary: The user sets system variables and monitors the EEG activity through the real-time interface (shown at left). All plots are updated in real time; both the EEG signal and the theta ratio (or network output) scroll across the screen. Although the system specs were designed to detect theta events (4-8 Hz frequency), the interface was generalized to classify any EEG frequency band of interest. Summary: The Spectral algorithm approximates a patient’s cognitive state using information from only one electrode. The advantage using this algorithm is speed – the system can iterate an entire processing cycle in less than 40 ms. Appropriate choice of electrode may justify this compromise. The network classification algorithm, while slower, can incorporate spatial information from all electrodes. The system is designed to give the user flexibility with the algorithm, selection of electrodes, and EEG frequency bands to generalize the system for a wide range of real-time experimental paradigms.

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Slide 3 :

ABSTRACT: Data acquisition for the Primordial Anisotropy Polarization Pathfinder Array (PAPPA) project balloon flight requires constant communication between the flight telescope and an onboard computer. However, conditions at flight altitude cause computers to become unreliable. A redundant computer control system, consisting of two identical computers connected to the telescope through a watchdog circuit, aims to deliver continuous computer control over the telescope. AUTHORS: Marc Blanco CTE ‘05 Shlomo Katz EE ‘05 ADVISOR: Dr. Mark Devlin Professor of Physics University of Pennsylvania DEMO TIMES: Thursday, April 21th, 2005 9:30, 10:30, 11:00, and 11:30 AM GROUP #4 UNIVERSITY OF PENNSYLVANIA Department of Electrical and Systems Engineering Redundant Flight Computer Control System for PAPPA Balloon Telescope SYSTEM OVERVIEW: Two Linux PC's are used to control and store data from external devices. Each computer sends and receives data via serial and parallel port interfaces, and can generate analog output voltages through a National Instruments card. The computers connect to the telescope through a watchdog circuit. Each computer sends and receives data as if it alone is in control of the system, but the watchdog chooses one computer whose control data is passed on to the telescope. Baseband Analog Bursts A screen capture of various signals at the moment when computer 1 fails. Shortly after failure, the computer is rebooted and telescope control is switched to computer 2 (whose control signal is 0V DC). Rebooting is triggered by grounding the reboot pin, which is seen here as a decrease in noise on (4). (For demonstration purposes, the control signal here comes from a function generator, not from computer 1. Thus, (1) is active even after the computer fails.) At this moment, computer 1 is being rebooted (one reboot light is on, one power light is off, and control has been switched to computer 2) Complete setup, involving watchdog circuit and two computers The watchdog circuit connects 16 telescope output lines to both computers, sends 28 input lines to the telescope from one of the computers, and continuously monitors the parallel port output from each computer. In the absence of a characteristic signal on one computer's parallel port, the watchdog reboots that computer and switches telescope control to the working computer. If necessary, extra watchdog circuit cards can be mounted on top of the primary card, and used to switch additional input and output lines based on the control decisions made by the primary watchdog circuit. SYSTEM IN ACTION: WATCHDOG CIRCUIT: The monitor subcircuit examines the computer’s output for correctness. If the output is incorrect, the countdown timer issues a momentary failure signal. This induces the computer power subcircuit to reboot the computer, and allows the switchover relays to choose the correct computer’s output to be sent to the telescope.

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	Medslides_PPT 1 Years ago. Category: Anesthesiology

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Electroencephalography (EEG) measures potential differences within the brain generated by the firin more Electroencephalography (EEG) measures potential differences within the brain generated by the firing of neurons. The signal can be characterized by its less

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