Neuronal Mechanisms of Perception and Cognition : an anthology of abstract models and experimental deductions in prevailing neuroscience paradigms

The thesis explores neural substrates functioning as complex networks in several predominant neuroscience paradigms, including motor imagery, flickering images, picture naming, semantic anomalies, auditory oddball, and motion-onset visual evoked potentials. The corresponding experimental designs and algorithms for recording electrophysiological data, such as magnetoencephalography (MEG) and electroencephalography (EEG), obtained during experimental tasks by the subjects are presented. We use standard tools and develop new physical and mathematical methods for data analysis. Models of underlying mechanisms of perception and cognition are discussed, proposed, tested, and compared with modern approaches and our own experiments. Key models / mechanisms include neural communication during motor imagery, generalised perceptual models, coherence resonance in visual perception, and the use of a neural network for object recognition. New brain-computer interface (BCI) applications are introduced, and existing systems are improved. Key suggestions include proper signal filtering for using artificial neural networks in BCI to classify imaginary movement, model-free estimation of brain noise, efficient BCI percepttracking using wavelet transforms, measuring voluntary attention performance, and real-time monitoring of pedestrian traffic based on wireless EEG data, as well as designing a cryptosystem, whose access is only possible through cognitive activity

Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study

The analysis of neurophysiological data using the two most widely used open-source MATLAB toolboxes, FieldTrip and Brainstorm, validates our hypothesis about the correlation between event-related coherence in the visual cortex and neuronal noise. The analyzed data were obtained from magnetoencephalography (MEG) experiments based on visual perception of flickering stimuli, in which fifteen subjects effectively participated. Before coherence and brain noise calculations, MEG data were first transformed from recorded channel data to brain source waveforms by solving the inverse problem. The inverse solution was obtained for a 2D cortical shape in Brainstorm and a 3D volume in FieldTrip. We found that stronger brain entrainment to the visual stimuli concurred with higher brain noise in both studies

Go for it

Mixning av Mikael Isakssons skiva Go for itUtgivning: Imogena records 2014Inspelad i OAL studio januari 2013Mixad av Tomas JohannessonMastrad av Claes PerssonMikael Isaksson, gitarrHans Backenroth, basJocke Ekberg, trummorLennart Simonsson, pianoLinn Hentschel, sång Låtar:Go For It - Mikael IsakssonBeyond Clouds - Mikael IsakssonSlow Down - Mikael IsakssonSambasso - Mikael IsakssonTaste That - Mikael IsakssonWhat's Up - Mikael IsakssonValiderad; 2014; 20131220 (tomjoh)</p

Machine learning approaches for classification of imaginary movement type by MEG data for neurorehabilitation

The conducted magnetoencephalographic (MEG) experiments with voluntary participants confirm the existence of two types of motor imagery, kinesthetic imagery (KI) and visual imagery (VI), distinguished by activation and inhibition of different brain areas. Similar to real movement, KI implies muscular sensation when performing an imaginary moving action that leads to event-related desynchronization (ERD) of motor-associated brain rhythms. By contrast, VI refers to visualization of the corresponding action that results in event-related synchronization (ERS) of α- and β-wave activity. A notable difference between KI and VI groups occurs in the frontal brain area. The application of artificial neural networks allows us to classify MI in raising right and left arms with average accuracy of 70% for both KI and VI using appropriate filtration of input signals

Neuronal pathway and signal modulation for motor communication

The knowledge of the mechanisms of motor imagery (MI) is very important for the development of braincomputer interfaces. Depending on neurophysiological cortical activity, MI can be divided into two categories: visual imagery (VI) and kinesthetic imagery (KI). Our magnetoencephalography (MEG) experiments with ten untrained subjects provided evidences that inhibitory control plays a dominant role in KI. We found that communication between inferior parietal cortex and frontal cortex is realised in the mu-frequency range. We also pinpointed three gamma frequencies to be used for motor command communication. The use of artificial intelligence allowed us to classify MI of left and right hands with maximal classification accuracy using the brain activity encoded in the identified gamma frequencies which were then proposed to be used for communication of specifics. Mu-activity was identified as the carrier of gamma-activity between these areas by means of phase-amplitude coupling similar to the modern day radio wave transmission