HypnoLarm Xiaoyang Ye, Pei Heng Zeng, Daniel He - PowerPoint PPT Presentation

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HypnoLarm Xiaoyang Ye, Pei Heng Zeng, Daniel He
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HypnoLarm Xiaoyang Ye, Pei Heng Zeng, Daniel He

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  1. HypnoLarm Xiaoyang Ye, Pei Heng Zeng, Daniel He The Preston M. Green Department of Electrical and Systems Engineering EEG electrodes spaced 1 inch apart, center-to-center in order to best replicate a clinical EEG setting. Abstract Methodology Analog Circuit Insulative polyurethane foam ensures only voltage data from the user’s scalp is recorded. Analog low pass filtering of EEG signals in the 1 – 30 Hz range. Multiplexing and sampling of EEG data using Arduino Mini processor unit. Digitally filter signal at cutoff of 30Hz. Cluster analysis of EEG signals using frequency domain parameters for sleep stage identification. It has been well established that sleep is a crucial factor for productivity and even health. While ideally people should sleep for at least 8 hours a day, modern changes in work habits and cultural stresses has significantly affected not only how much we sleep, but also how we sleep. HypnoLarm targets this issue, and strives to make the most out of the sleep we get by addressing the problem from a modernistic ergonomic viewpoint. EEG signals have magnitude in the microvolt range. A much larger voltage magnitude is needed to detect changes in the signal. We used an instrumentation amplifier with Rg = 560Ω to get a gain of 89.2. The Arduino Mini Pro analog inputs accept voltages in the 0 – 5V range. Since EEG signals can have negative voltages, this poses a problem for the Arduino during reading. To get around this, we use a non-inverting summing circuit to shift the EEG signal from the -2.5 – 2.5V range into the 0 – 5V range. The summing circuit is currently unity gain, but offers another stage for us to introduce more gain if we need to in the future. Shielded wire does not eliminate all of the noise present in our signal. In the next analog stage, we implement a low pass filter to eliminate frequencies outside our desired range. As mentioned previously, our frequency range of interest is the 1 – 30 Hz range. The second order filter has a cutoff frequency of 29Hz. Following the low pass filter, we have a unity gain buffer that transforms the high impedance input into a low impedance input before we enter the Arduino. Central Arduino microprocessor unit performs multiplexing and digital low-pass filtering of EEG sensor reading, then sends data array to alarm module via Bluetooth. Overview Final Stage It has been proven that waking up during different sleep stages has profound and significant effects on an individual’s alertness and energy levels. HypnoLarm is a sleep optimization system that aims to use “free-form” electroencephalography (EEG) to continuously track and analyze a user’s sleep stage in real time. It can then optimizes waking for users around their desired set time through a combination of sleep stage detection and prediction. The HypnoLarm design comprises two separate modules operating in tandem with each other through a wireless communications protocol: • The sensor sheet An array of dry EEG sensors encased in foam to accurately record EEG signals during sleep. • The alarm module Processor unit which receives EEG data wirelessly from the sensor sheet, and analyzes the signals to identify sleep stages. The algorithm tracks the user’s sleep over the entire night, and determines an optimal time to wake the user around his or her desired time through a combination of sleep stage prediction and detection. Sensor Sheet Final Design Alarm Algorithm During the course of this project, a cluster-analysis method was chosen to identify sleep stages from EEG data samples. This method allows for an abstraction of using signal properties to determine state. The clustering method first receives instruction for EEG signal feature parameters to extract, which may or may not be deterministic in sleep stage identification. A set of pre-scored EEG sleep data is then fed into the algorithm, and their features extracted. The algorithm then categorizes the feature vector values based on the sleep stage (cluster) for that particular sample. This is followed by PCA analysis which identifies and calculates 3 eigenvectors, from the feature parameters, that provide the greatest intra-cluster differentiation. Each sample is then plotted on a 3-D vector space and color coded for its corresponding sleep stage, with the 3 eigenvectors as the axis. Visible clusters of points for each stage of sleep is formed, and sleep stage can be identified by the cluster a sample point falls into. EEG Signal Collection For this project, we used the Biopac Ag-AgCl dry EEG electrodes. Using CAD and 3D printing, we designed and printed electrode caps to cover and attach the shielded wire leads to the electrodes. Design Requirements Stage Scoring Using Clusters Sleep Stage Clusters Formed Using Pre-scored Sleep EEG Data Instrumentation Amplifier Summing Circuit Biopac electrode Overall Alarm Algorithm flow chart