Gauze pad separated from four layer thickness to two layer thickness and trimmed to 3.5 cm × 2.5 cm.Ĭollodion (produced by Mavidon Medical available from MVAP and Integra Neurosupplies)Īir compressor (20 PSI output) with foot pedal switch and ~4 m of tubing attached to an electrode applicator tip. Subject who has provided informed consent and is dressed in sleeping attireĪ well ventilated room for electrode applicationĪbrading cream, such as NuPrep, SkinPure, or Lemon Prep from a supplier such as Integra NeuroSupplies or MVAP.Įlectrode conductive paste, such as Ten20 (MVAP and Integra Neurosupplies)Įlectrode conductive gel, such as Signa gel (MVAP and Integra Neurosupplies)ĮEG electrodes – gold or silver/silver chloride10 mm diameter cup electrodes with a 48” wire lead with safety connector (Grass, MVAP and Integra Neurosupplies) Materials (Rather than metric units, materials are listed in commonly available dimensions.) Note: Research involving human subjects should adhere to all local and national regulations that ensure protection of human subjects. This protocol assumes that the subjects have been appropriately screened and are suitable for study. The subject's sleep schedule in the days prior to recording can greatly affect the sleep and EEG on the recording night, as can various illegal, prescription, and over the counter drugs. The EEG recorded on a study night depends strongly on the subject's history prior to recording. The following protocol describes calibration and EEG recording on an ambulatory recorder, but a clinical recorder in a laboratory could substitute for an ambulatory recorder. Computer digitization and recording have replaced paper recording, and handheld ambulatory recorders now can replace whole racks of amplifiers. EEG amplifiers and recording instruments have changed greatly in the past 20 years. The protocol provides instruction on EEG recorder calibration, electrode application, EEG recording, and spectral analysis of the EEG. Although the focus is on use for sleep research, the methods can be adapted for other fields of neuroscience investigation. This unit presents methods for recording and analyzing the human electroencephalogram (EEG). Finally, results for the individual data and mixed data are tabulated and presented in the following thesis.EEG Recording and Analysis for Sleep Research The SVM model randomly selects the training and testing data and accordingly gives results for the accuracy. Data from the 10 individuals is processed individually and also after being mixed. One-third of the total data sets is used for training the SVM model and then the rest of the data sets are used to test the accuracy of the model. The data was divided into two sets, namely. This part of this data was then used to design a Machine Learning model called Support Vector Machine (SVM) on Matlab, using which the data was then processed. All the data was collected with complete supervision and without any kind of movement of the subject during the collection process. The subjects were asked to imagine moving an object towards the direction &lsquoRight&rsquo for the first 150 data sets collected and then the same for the direction &lsquoLeft&rsquo for another 150 data sets. Any remaining noise from the signal can be removed by passing it through a digital low-pass filter, if required.ĮEG data is collected from 10 people while they are made to concentrate on a particular thought. The data is sampled at a sampling rate of 838 Hz. The EEG signal is observed on the cathode-ray oscilloscope and the data is collected on an Arduino Uno microcontroller using the Hyperterminal software. The gain of the entire circuit is about 5140 V/V.
Each of these stages contribute in their own way to amplify and also filter the noise from the EEG signal. This circuit consists of five stages, namely, the Instrumentation Amplifier, 60 Hz Notch Filter, 31Hz Low Pass Filter, Gain Stage, and Clamper Circuit. Since the signal measures in the unit of micro-volts it needs to be amplified using amplifier circuit. The EEG data is collected using three EEG electrodes, each being the positive, negative and ground terminals respectively. The objective of this project is to collect Electroencephalography (EEG) signals through wireless sensors, and the process the collected signals through machine learning methods. Some machine learning systems try to eradicate the need for human intuition in data analysis whereas others embrace a collective approach between humans and machines. Machine learning methods are an excellent way for understanding the neural basis of human decision making.