Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison

This paper compares two Self-Organizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a significant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments.     

Linear classification of low-resolution EEG patterns produced by imagined hand movements.

Electroencephalograph (EEG)-based brain-computer interfaces (BCI's) require on-line detection of mental states from spontaneous EEG signals. In this framework, surface Laplacian (SL) transformation of EEG signals has proved to improve the recognition scores of imagined motor activity. The results we obtained in the first year of an European project named adaptive brain interfaces (ABI) suggest that: 1) the detection of mental imagined activity can be obtained by using the signal space projection (SSP) method as a classifier and 2) a particular type of electrodes can be used in such a BCI device, reconciling the benefits of SL waveforms and the need for the use of few electrodes. Recognition of mental activity was attempted on both raw and SL-transformed EEG data from five healthy people performing two mental tasks, namely imagined right and left hand movements.     

Effect of alcohol on exercise-induced changes in serum glucose and serum free fatty acids

Electrochemical measurements show that there are high-potential states of two copper proteins, Pseudomonas aeruginosa azurin and Thermus thermophilus CuA domain; these perturbed states are formed in guanidine hydrochloride (GuHCl) solution in which the proteins are still blue (azurin) and purple (CuA). In each case, the high-potential state forms reversibly. Absorption (azurin, CuA), visible circular dichroism (azurin, CuA), resonance-Raman (CuA), and EPR (CuA) spectra indicate that the structure of the oxidized copper site of each high-potential form is very similar to that of the native protein. It is proposed that GuHCl perturbs one or more H-bonds in the blue or purple copper active site, thereby allowing Cu(I) to adopt a more favorable coordination structure than that in the rigid cavity of the native protein.     

A local neural classifier for the recognition of EEG patternsassociated to mental tasks

This paper proposes a novel and simple local neural classifier for the recognition of mental tasks from on-line spontaneous EEG signals. The proposed neural classifier recognizes three mental tasks from on-line spontaneous EEG signals. Correct recognition is around 70%. This modest rate is largely compensated by two properties, namely low percentage of wrong decisions (below 5%) and rapid responses (every 1/2 s). Interestingly, the neural classifier achieves this performance with a few units, normally just one per mental task. Also, since the subject and his/her personal interface learn simultaneously from each other, subjects master it rapidly (in a few days of moderate training). Finally, analysis of learned EEG patterns confirms that for a subject to operate satisfactorily a brain interface, the latter must fit the individual features of the former.     

EEG and MEG brain-computer interface for tetraplegic patients.

We characterized features of magnetoencephalographic (MEG) and electroencephalographic (EEG) signals generated in the sensorimotor cortex of three tetraplegics attempting index finger movements. Single MEG and EEG trials were classified offline into two classes using two different classifiers, a batch trained classifier and a dynamic classifier. Classification accuracies obtained with dynamic classifier were better, at 75%, 89%, and 91% in different subjects, when features were in the 0.5-3.0-Hz frequency band. Classification accuracies of EEG and MEG did not differ.