WT As Preprocessor For Neural Network Expretation Of EEG Spikess

We have used support vector machine (SVM) for classification task. We have used RBF kernel for training the classifier. 10 fold cross-validation is used for determining cost parameter C and best kernel width for RBF kernel function. If we perform classification without any feature selection or feature extraction then the accuracy is 48.99% and 65.82% for AVIRIS and HYDICE image respectively which is very poor and it highly motivates us to apply feature reduction technique. In table II we have shown the classification accuracy for each of the pair of class for PCA, MI and PCA-QMI.

Whenever a seizure occurs, it forms a rhythmic activity. A typical EEG with seizure is given in Figure 1

This research article was published in the journal of Alzheimer’s disease and Parkinsonism on August 26th 2013, volume 3, issue 2. Dr.Perea et al. conducted the study at department of neurology, Alzheimer’s Research Disease Center, department of Psychology, Bioengineering program-department of engineering, at University of Kansas School of Medicine, Kansas City, KS, USA.

Many epilepsy patients, especially in developing countries, can’t afford current seizure monitoring devices. One of the main priorities of the neural cap monitoring system is to offer an economically feasible device to epilepsy patients, in specific, epilepsy patients in developing countries. Subsequently, many Epilepsy Monitoring Units (EMU) are high cost, and rely in only one biomarker to detect when a seizure is occurring [2][3][4][5]. For example, the EMOTIV EPOC+, an EEG data sensor, offers an advanced brain computer interface at a starting price of $799.00 [5]. This product only focuses on EEG signals and doesn’t rely on other epilepsy biomarkers to correlate its readings. Another product, The SAMi Sleep Monitoring System, have a

Stage 1 is the Electroencephalogram (EEG). The EEG provides a summary of electro activity from a certain area of the brain. The EEG is also the sum of the electrical activity of all neurons between the two recording electrodes (Garrett,2015). Stage 1 is when one is feeling drowsy and transitioning into a state of sleep (Thien Thanh Dang-Vu, 2017).

Abstract – Epilepsy is an inveterate neurological disorder which is caused by unusual nerve cell activity in the brain and is characterized by consecutive unexpected seizures. Precisely indentifying and enumerating the seizures in patients with epilepsy is essential for diagnosis, selecting the treatment and estimating the effects of the therapy. Epilepsy detection done just by physically anatomizing a person’s body is a very arduous job as it involves peculiar nerve cell activity in the brain. These brain signals can be utilized to detect epileptic seizure in a subject by analyzing it with the aid of an EEG sensor. Thus EEG monitoring is used for the appropriate diagnosis, classification and detection of the seizure. This paper presents an approach to examine the brain signal using an EEG sensor and carry out various signal processing techniques on it in MATLAB, detect its

Abstract—Spike sorting, the method which could separate multiple neuron signals contained by the extracellular recordings, has been widely used in neuronal activity studies. In this project, we successfully applied our own primitive spike sorter to analyze extracellular data from recordings in the primary motor cortex of nonhuman primate. We first built the spike sorter using principal components analysis (PCA), and then clustered the data using k-means clustering method. We also compared the waveforms of the first day to the waveforms of the second day. Our results showed that spike sorting could be successfully realized by PCA and k-means clustering. Besides, we also found that the first day’s data and the second day’s data probably are obtained

To justify the idea the proposed, a series of experiments are undertaken. This consists of implementing the system into a real smart home with a single power analyser at the main electrical panel. The assistive system was tested rigorously against real life scenarios, related to morning ADL to test the accuracy and how competent the system is to assist patients in a real life context, algorithm for assistance is implemented within the system for the activity recognition every time an electrical appliance is used. The authors determined potential errors or abnormal situations to test out any inconsistent behaviour which should cause a prompt to be sent to the patient to provide the appropriate

The EEG was recorded from 64 electrodes under eyes, IO1, IO2, on the mid-line, Fp1, Fp2, AFz, Fz, Cz, CPz, Pz, POz, Oz, and Iz; on the left side, F9, F7, F5, F3, F1, FT7, FC5, FC3, FC1, T7, C5, C3, C1, TP9, TP7, CP5, CP3, CP1, P9, P7, P5, P3, P1, PO9, PO7, and O1; on the right, F2, F4, F6, F8, F10, FC2, FC4, FC6, FT8, C2, C4, C6, T8, CP2, CP4, CP6, TP8, TP10, P2, P4, P6, P8, P10, PO8, PO10, and O2; from the international 10-20 system (American Electroencephalographic Society, 1994). Individual sensors were adjusted until impedances

Wearable EEG signal processors are imminently expected by researchers, clinical doctors and patients [127–131]. One of the main

The reason why a BCI works is all because of the way our brain functions. Our brain is filled with neurons. The neurons are at work when we move, think, feel or remember something. The work is completed by small electrical signals that zip from neuron to neuron as fast as 250mph. Now scientists can detect the signals, interpret them and make them to direct a device. Electroencephalography (EEG) is a method used in measuring the electrical activity of the brain.

The AD8232 is an integrated signal conditioning block for Electrocardiogram, which is designed for extraction, amplification and filtering of small biopotential signals in the presence of noisy conditions, created by motion or remote electrode placement. The AD8232 has essential pins like shutdown pin, leadoff+, leadoff-, analog output, 3.3v, gnd to operate with the Arduino Mega 2560. There also exist RA(Right Arm),LA(Left Arm), RL(right leg) pins to attach to the custom sensor of human body . It has an led indicator which pulsates with the rhythm of a heartbeat

This report addresses the different steps required for the collection of these signal and their correct interpretation into real commands such as: preprocessing, feature extraction and classification. The report continues with discussion of the various applications of the BCI devices, including description of some the commercial devices and their characteristics and concludes with an overview of the trends and challenges of BCI.

From Figure 2, it is clear that the values predicted by bagging, boost and neural network methods came very close to actual values. On the other hand, the SVM with radial basis function (RBF) kernel and SVM with 3 order of polynomial model generated a horizontal straight line, i.e. did not change at all. Similar trends (shown in Figure 4) were observed for the rest of the parameter sets. This shows that SVM has a poor performance in general as compared to other methods.

Now a day’s technology is running with time, it completely occupied the life style of human beings. It is used everywhere in our daily life to fulfill our requirements. In this project using advanced technology to sense serious health problems so that efficient medical services are provided to the patient in appropriate time.