In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are thre...

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this project, the multivariate empirical mode decomposition (MEMD)method will be proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. Firstly, the EEG signals will be decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-rel...

In spite of availability of various approaches, the control of prosthetic limb would be more effective if it is based on Electromyogram (EMG) signals from remnant muscles and Electroencephalogram (EEG). The analysis of these signals depends on various factors such as amplitude, time and frequency domain properties. EEG signals are obtained from the experiments conducted in Biomedical laboratory...

Recently, the correlation between biomedical signals, such as electroencephalograms (EEG) and electrocardiograms (ECG) time series signals, has been analysed using the Pearson Correlation method. Although Wavelet Transformations (WT) have been performed on time series data including EEG and ECG signals, so far the correlation between WT signals has not been analysed. This research shows the cor...

In epileptic patients candidate to surgery, the interpretation of EEG signals recorded either within (depth EEG) or at the surface (scalp EEG) of the head is a crucial issue to determine epileptogenic brain regions and to define subsequent surgical strategy. This task remains difficult as there is no simple relationship between the spatiotemporal features of neuronal generators (convoluted cort...

OF THESIS DIMENSIONALITY REDUCTION AND CLASSIFICATION OF TIME EMBEDDED EEG SIGNALS Electroencephalogram (EEG) is the measurement of the electrical activity of the brain measured by placing electrodes on the scalp. These EEG signals give the micro-voltage difference between different parts of the brain in a non-invasive manner. The brain activity measured in this way is being currently analyzed ...

In the paper, we use Microsoft’s Visual Studio Development Kit and the C# programming language to implement chaos-based electroencephalogram (EEG) encryption system with three encryption levels. A chaos logic map, an initial value, and a bifurcation parameter for the map are used to generate level I chaosbased EEG encryption bit streams. Two encryption-level parameters are added to these elemen...

The effect of voluntary and involuntary eyeblinks in independent components (ICs) contributing to electroencephalographic (EEG) signals was assessed to create templates for eyeblink artifact rejection from EEG signals with small number of electrodes. Fourteen EEG and one vertical electrooculographic signals were recorded for twenty subjects during experiments that prompted subjects to blink vol...

Disease identification is a major task in the field of biomedical. To perform it the analysis of EEG signal is to be performed. The proposed method presents for feature extraction from electroencephalogram (EEG) signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the EMD gives an effective time-frequency analysis of nonstationary signals. The intrinsic mode f...

BACKGROUND This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. METHODS We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volu...