Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques.

Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accurate diagnosis and appropriate drug therapy to patients. This work aims to categorize electroencephalography signals on different channels' recordings for classifying and predicting epileptic seizures. The collection of the electroencephalography recordings contained in the dataset attributes 179 information and 11,500 instances. Instances are of five categories, where one is the symptoms of epilepsy seizure. We have used traditional, ensemble methods and deep machine learning techniques highlighting their performance for the epilepsy seizure detection task. One dimensional convolutional neural network, ensemble machine learning techniques like bagging, boosting (AdaBoost, gradient boosting, and XG boosting), and stacking is implemented. Traditional machine learning techniques such as decision tree, random forest, extra tree, ridge classifier, logistic regression, K-Nearest Neighbor, Naive Bayes (gaussian), and Kernel Support Vector Machine (polynomial, gaussian) are used for classifying and predicting epilepsy seizure. Before using ensemble and traditional techniques, we have preprocessed the data set using the Karl Pearson coefficient of correlation to eliminate irrelevant attributes. Further accuracy of classification and prediction of the classifiers are manipulated using k-fold cross-validation methods and represent the Receiver Operating Characteristic Area Under the Curve for each classifier. After sorting and comparing algorithms, we have found the convolutional neural network and extra tree bagging classifiers to have better performance than all other ensemble and traditional classifiers.

Molecular orbitals of delocalized electron clouds in neuronal domains.

We have further developed the two-brains hypothesis as a form of complementarity (or complementary relationship) of endogenously induced weak magnetic fields in the electromagnetic brain. The locally induced magnetic field between electron magnetic dipole moments of delocalized electron clouds in neuronal domains is complementary to the exogenous electromagnetic waves created by the oscillating molecular dipoles in the electro-ionic brain. In this paper, we mathematically model the operation of the electromagnetic grid, especially in regard to the functional role of atomic orbitals of dipole-bound delocalized electrons. A quantum molecular dynamic approach under quantum equilibrium conditions is taken to illustrate phase differences between quasi-free electrons tethered to an oscillating molecular core. We use a simplified version of the many-body problem to analytically solve the macro-quantum wave equation (equivalent to the Kohn-Sham equation). The resultant solution for the mechanical angular momentum can be used to approximate the molecular orbital of the dipole-bound delocalized electrons. In addition to non-adiabatic motion of the molecular core, 'guidance waves' may contribute to the delocalized macro-quantum wave functions in generating nonlocal phase correlations. The intrinsic magnetic properties of the origins of the endogenous electromagnetic field are considered to be a nested hierarchy of electromagnetic fields that may also include electromagnetic patterns in three-dimensional space. The coupling between the two-brains may involve an 'anticipatory affect' based on the conceptualization of anticipation as potentiality, arising either from the macro-quantum potential energy or from the electrostatic effects of residual charges in the quantum and classical subsystems of the two-brains that occurs through partitioning of the potential energy of the combined quantum molecular dynamic system.

Genomic instantiation of consciousness in neurons through a biophoton field theory.

A theoretical framework is developed based on the premise that brains evolved into sufficiently complex adaptive systems capable of instantiating genomic consciousness through self-awareness and complex interactions that recognize qualitatively the controlling factors of biological processes. Furthermore, our hypothesis assumes that the collective interactions in neurons yield macroergic effects, which can produce sufficiently strong electric energy fields for electronic excitations to take place on the surface of endogenous structures via alpha-helical integral proteins as electro-solitons. Specifically the process of radiative relaxation of the electro-solitons allows for the transfer of energy via interactions with deoxyribonucleic acid (DNA) molecules to induce conformational changes in DNA molecules producing an ultra weak non-thermal spontaneous emission of coherent biophotons through a quantum effect. The instantiation of coherent biophotons confined in spaces of DNA molecules guides the biophoton field to be instantaneously conducted along the axonal and neuronal arbors and in-between neurons and throughout the cerebral cortex (cortico-thalamic system) and subcortical areas (e.g., midbrain and hindbrain). Thus providing an informational character of the electric coherence of the brain - referred to as quantum coherence. The biophoton field is realized as a conscious field upon the re-absorption of biophotons by exciplex states of DNA molecules. Such quantum phenomenon brings about self-awareness and enables objectivity to have access to subjectivity in the unconscious. As such, subjective experiences can be recalled to consciousness as subjective conscious experiences or qualia through co-operative interactions between exciplex states of DNA molecules and biophotons leading to metabolic activity and energy transfer across proteins as a result of protein-ligand binding during protein-protein communication. The biophoton field as a conscious field is attributable to the resultant effect of specifying qualia from the metabolic energy field that is transported in macromolecular proteins throughout specific networks of neurons that are constantly transforming into more stable associable representations as molecular solitons. The metastability of subjective experiences based on resonant dynamics occurs when bottom-up patterns of neocortical excitatory activity are matched with top-down expectations as adaptive dynamic pressures. These dynamics of on-going activity patterns influenced by the environment and selected as the preferred subjective experience in terms of a functional field through functional interactions and biological laws are realized as subjectivity and actualized through functional integration as qualia. It is concluded that interactionism and not information processing is the key in understanding how consciousness bridges the explanatory gap between subjective experiences and their neural correlates in the transcendental brain.