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Introduction
Published in Narayan Panigrahi, Saraju P. Mohanty, Brain Computer Interface, 2022
Narayan Panigrahi, Saraju P. Mohanty
Brain imaging techniques or neuroimaging techniques allow doctors and researchers to view activity or problems within the human brain, without invasive neurosurgery. There are a number of accepted, safe imaging techniques in use today in research facilities and hospitals throughout the world. Prominent brain imaging techniques that are available to cognitive neuroscientists, including positron emission tomography (PET), near infrared spectroscopy (NIRS), magnetoencephalogram (MEG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). We discuss most of the available neuroimaging techniques in this section but focus on EEG and fMRI because they are the most widely used techniques.
Overview of pervasive computing
Published in Sonali Goyal, Neera Batra, N.K. Batra, An Integrated Approach to Home Security and Safety Systems, 2021
Artificial Neural Networks (ANNs) have been constructed with a structure similar to the human brain. The weight of an adult human brain weighs approximately 1.5 kg with a volume of 1260 cubic centimetres. The brain is composed of 200 billion neurons connected by 25 trillion synapses, glial cells and blood vessels. The neuron can be separated into three parts: cell body (soma), the dendrites and the axon as shown in Figure 1.34.
Application of Machine-Learning Techniques in Electroencephalography Signals
Published in Mridu Sahu, G. R. Sinha, Brain and Behavior Computing, 2021
Arun Sasidharan, Kusumika Krori Dutta
The brain is the main “biological computer” of our body, capable of enormous amounts of information processing, storage, and complex predictions. The human brain brings about a myriad of functions, including the processing of sensory information from external and internal sources, controlling body movements, and bringing about cognitive functions such as ability to think, learn new ideas, memorize facts, remember past events, speak to other people, and make complex decisions. Different parts of the brain specialize in different sub-functions. Some of the evolutionarily conserved brain regions control basic bodily activities like respiration (brainstem), hormone regulation (hypothalamus), information flow (thalamus), etc. Whereas, the cerebral cortex is a brain region that is more evolved and enlarged (forming the bulk of brain), with specialization for auditory processing (in temporal cortex), visual processing (in occipital cortex), touch processing (in parietal cortex), movement (in frontal cortex), thinking (in pre-frontal cortex), so on and so forth.
Generating security questions for better protection of user privacy
Published in International Journal of Computers and Applications, 2020
Armin Anvari, Lei Pan, Xi Zheng
First of all, it is vital to find out how human’s memory works. Human memory is a binary process which can be categorized into two types of short-term memory and long-term memory. Information can be stored in short-term memory from few minutes to few days, which and communicate with the long-term memory which information can last up to many years. An adult human brain contains 100 billion of neurones. Numbers of these neurones involve in recalling, storing, and consolidating experiences, in both sleeping and waking condition of a human. This procedure of storing, retaining, and recalling information is ‘memory’ [21].
Epileptic seizure anticipation and localisation of epileptogenic region using EEG signals
Published in Journal of Medical Engineering & Technology, 2018
Aarti Sharma, J. K. Rai, R. P. Tewari
Epilepsy is one of the most common brain disorder [1]. There is no permanent cure for epilepsy. The regular intake of the antiepileptic drugs (AED) is the only prerogative for treating epilepsy. AED are not equally effective in all cases and hence only possible treatment is surgical resection of the epileptogenic region. AED is more effective when administered to the patient just before the seizure onset. In addition, the ill effect of the seizures can be minimised early prediction of seizure onset [2]. There are two standard non-invasive techniques for human brain investigation namely functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) signals. EEG signals have high temporal resolution and acquisition process is easy as well as cost effective compared to fMRI images [3]. So, EEG signals have been used for seizure prediction and identification of epileptogenic region in this work. The brain signal undergoes significant changes before the seizure onset. This change in the brain signal can be observed up to 40 min before the seizure onset and this state is referred as pre-ictal state [4]. EEG signal acquisition is affected by line noise and muscle and eye blink artefacts [5]. Hence pre-processing of EEG signal is required. There are many pre-processing methods available such as common averaging reference (CAR), surface Laplacian (SL), independent component analysis (ICA), principal component analysis (PCA) etc. [6]. It is reported in [7] that ICA is highly efficient in computation and better in performance for artefacts removal. So, ICA has been used in this work for artefacts removal. The information related to epileptic seizure is generally confined to specific frequency bands. The five frequency bands delta (0–4 Hz), theta (4–8 Hz), alpha (8–16 Hz), beta (16–32 Hz) and gamma (32–128 Hz) are known to provide more details about various neuronal activities [8]. Various linear and non-linear parameters has been used to detect and predict epileptic seizure such as correlation, largest Lyapunov exponent of band limited EEG signals, approximate entropy, sample entropy, variance, Hurst exponent, Hjorth parameter and accumulated energy [9,10]. Seizure prediction using power spectral density from different frequency bands has been reported in [11]. The main drawback of power spectral density is that it increases both during pre-ictal and inter-ictal state which lowers the sensitivity.