|Filtering and removal of artifacts in Biosignals|
This lab focuses on understanding and analyzing common artifacts present in an EEG dataset and to understand various filtering methods for artifact removal from biosignals
|Point processes and models|
This lab focuses to understand Spiking Neural Networks (SNN) or Artificial Neural Networks (ANN) and the point process models like AdEx model which produces different spiking behaviors that mimics biological neural networks using a web-based platform.
|Analysis of Biosignals activity and artifacts|
This lab focuses to understand the basic concepts of biosignal analysis, characteristics of biosignal in time and frequency domain, different types of artifacts in biosignal and the web-based platform allows to perform noise filtration and data separation method like Independent Component Analysis (ICA).
|Power spectrum calculations using different windows|
This lab focuses to understand the signal processing algorithm-Fast Fourier transform (FFT), the distribution of frequency in an EEG signal at different time bins and to visualize the brain rhythms at specific time bins for different cognitive tasks using a browser-based platform.
|Study the changes in the PSDs by varying window width|
This lab focuses to understand the power spectrum distribution of brain rhythms in different brain regions, the event-related EEG epochs for specific sensory and cognitive tasks and performing the window analysis of EEG signals to visualize the Power spectrum density (PSD) of brain waves in a shorter time segment window using a web-based platform.
|Temporal structure in EEG|
This lab focuses to understand the temporal characteristics of an EEG data, concepts of spatio-temporal changes of an event related EEG signals and the web based platform allows to generate and visualize an Event-related potential (ERP) of EEG Biosignal at specific time bins that evoked in response to specific sensory, cognitive and motor tasks.
|Motor unit firing pattern|
This lab focuses to understand the basic concepts of Motor Neurons (MNs) and Motor units and the web-based platform can generate Multiple firing patterns of motor units by specific external stimuli.
|Modeling network activity as in biological circuits|
This lab focuses to understand the basic concepts of biological neural networks and artificial neural networks, the computational algorithms for reconstructing neural network activity and generating the spiking neurons that mimics biological neural circuits using a web-based platform.
|Modeling synaptic network connectivity|
This lab focuses to understand the basic concepts of neuron synaptic communications and its dynamics, replication of neural network activity by different synaptic connections and generating multiple firing patterns of neuronal spikes that mimics biological synaptic network connectivity using a browser-based platform.
|Reconstructing Averaged Population Response|
This lab focuses on understanding the basic concepts of neuronal population and its structural activity and the web based platform allows to reconstruct an average neuronal population response and local field potential(LFP) by mathematical modeling.
|Biosignal Import and Channel Analysis|
This lab focuses on understanding basics of EEG signals, identifying channel locations and visualization of raw EEG data using browser based platform.
|Time-frequency analysis of Biosignals|
This lab focus to visualize the distribution of power into frequency components spectrum of the provided data using browser based platform.
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