In this case, a combination of hardware and software filtering worked best. One relevant one, however, deals with how we filter out brain wave frequencies that fall outside the alpha and beta ranges. Hardware Software TradeoffsĪs a primarily analog project, this EEG system does not have too many hardware-software tradeoffs. This ratio is then passed through a 3-point averaging filter over time, and the resulting average ratio is used to light up red, yellow, and green LEDs, depending on the user's state. After obtaining the peak frequency of the FFT spectrum and summing up certain bins, we can obtain a ratio of "calm" waves to "excited" waves. For safety reasons, we could not connect the circuit to any AC power source, so using an oscilloscope was not a possibility. Once we obtain 512 ADC samples from the hardware, we take an FFT of the data while simultaneously displaying the wave on the TFT screen. Our last hardware stage involves a voltage divider to ensure that the signal is between 0 and 3.3V so that the ADC on the PIC32 can read it. After two gain stages, we have two filtering stages to filter out all frequencies above 31 and below 8 Hz, placing us within alpha and beta wave ranges. From the electrodes themselves, we expect a signal on the order of microvolts to millivolts, so a high gain is necessary. Because all the amplifiers used in this project are dual-rail, and because we cannot use an AC power source, we built a dual-rail battery circuit to get +4.5 and -4.5 V rails. To ground the circuit properly, we take take another electrode measurement from the user's arm to the circuit ground rail. After placing adhesive electrodes on a user's forehead, we use jumper cables to connect to the negative and positive ports of an instrumentation amplifier. The hardware of this EEG involves 5 stages, with a combined gain of 1250. Logical Structureįigure 1 below shows the high level block diagram of the EEG system. In this case, it allows us to understand what the peak frequency of our brain waves, as well as how much our brain waves are dominated by alpha waves, and how much by beta waves. The conversion from fixed-point to decimal is shown below.įFTs are utilized in this EEG project to understand the frequency characteristic of a signal. In 16:16 fixed-point representation, we take the least significant 16 bits as the fractional portion of a number, and the most significant 16 bits as the non-fractional portion. 16:16 fixed-point arithmetic is preferable to floating point arithmetic primarily because it is much faster, and because we can represent these numbers as 32-bit integer types. The primary math behind this project involves understanding of Fast Fourier Transforms (FFTs) and fixed point arithmetic.
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