Janez Zidar

Amyotrophic lateral sclerosis patients show executive impairments on standard neuropsychological measures and an ecologically valid motor-free test of executive functions

The study aimed to evaluate the nature and extent of executive deficits in nondemented amyotrophic lateral sclerosis (ALS) patients. A total of 22 ALS patients and 21 matched controls were compared on standard neuropsychological tests of executive functions with appropriate control for motor impairment and on an ecologically valid motor-free test of executive functions, the Medication Scheduling Task (MST). Our results show that motor dysfunction can present a significant confound when using standard neuropsychological measures; however, even when accounting for motor disabilities, ALS patients show a robust pattern of cognitive dysfunctions. Additionally, MST was shown to be a sensitive measure of cognitive impairment, providing an important insight into cognitive processes relevant for patients daily living.

Sensitivity of motor unit potential analysis in facioscapulohumeral muscular dystrophy

Template‐operated motor unit potential (MUP) analysis has made quantitative electromyography (EMG) feasible, even in busy laboratories, but validation of this approach is still necessary. In the present study, the utility of multi‐MUP analysis was assessed in patients with a molecular genetic diagnosis of facioscapulohumeral muscular dystrophy (FSHD). Manual assessment of muscle strength and concentric‐needle EMG of the biceps brachii and vastus lateralis muscles were performed. The sensitivity for diagnosing myopathy (mean values and outliers) was tested for eight MUP parameters and four of their combinations. The group comprised 31 patients. Elbow flexion and knee extension strength was normal in 45% and 52% of patients, respectively. The most sensitive MUP parameter was thickness, followed by duration. A combination of three MUP parameters (thickness, amplitude, and duration/area) was needed for maximal sensitivity. The study demonstrated a high sensitivity of multi‐MUP analysis in FSHD. Myopathic abnormalities were demonstrated in all weak biceps brachii muscles, and in 77% of biceps brachii muscles with normal strength. Muscle Nerve, 2006

Using ANNs to predict a subject's response based on EEG traces

Numerous reports have shown that performing working-memory tasks causes an elevated rhythmic coupling in different areas of the brain; it has been suggested that this indicates information exchange. Since the information exchanged is encoded in brain waves and measurable by electroencephalography (EEG) it is reasonable to assume that it can be extracted with an appropriate method. In our study we made an attempt to extract the information using an artificial neural network (ANN), which can be considered as a stimulus-response model with a state observer. The EEG was recorded from three subjects while they performed a modified Sternberg task that required them to respond to each task with the answer true or false. The study revealed that a stimulus-response model can successfully be identified by observing phase-demodulated theta-band EEG signals 1 s prior to a subjects answer. The results also showed that it was possible to predict the answers from the EEG signals with an average reliability of 75% for all the subjects. From this we concluded that it is possible to observe the system states and thus predict the correct answer using the EEG signals as inputs.

Identification of the phase code in an EEG during gripping-force tasks: A possible alternative approach to the development of the brain-computer interfaces

BACKGROUNDnThe subject of brain-computer interfaces (BCIs) represents a vast and still mainly undiscovered land, but perhaps the most interesting part of BCIs is trying to understand the information exchange and coding in the brain itself. According to some recent reports, the phase characteristics of the signals play an important role in the information transfer and coding. The mechanism of phase shifts, regarding the information processing, is also known as the phase coding of information.nnnOBJECTIVEnThe authors would like to show that electroencephalographic (EEG) signals, measured during the performance of different gripping-force control tasks, carry enough information for the successful prediction of the gripping force, as applied by the subjects, when using a methodology based on the phase demodulation of EEG data. Since the presented methodology is non-invasive it could be used as an alternative approach for the development of BCIs.nnnMATERIALS AND METHODSnIn order to predict the gripping force from the EEG signals we used a methodology that uses subsequent signal processing methods: simplistic filtering methods, for extracting the appropriate brain rhythm; principal component analysis, for achieving the linear independence and detecting the source of the signal; and the phase-demodulation method, for extracting the phase-coded information about the gripping force. A fuzzy inference system is then used to predict the gripping force from the processed EEG data.nnnRESULTSnThe proposed methodology has clearly demonstrated that EEG signals carry enough information for a successful prediction of the subjects performance. Moreover, a cross-validation showed that information about the gripping force is encoded in a very similar way between the subjects tested. As for the development of BCIs, considering the computational time to pre-process the data and train the fuzzy model, a real-time online analysis would be possible if the real-time non-causal limitations of the methodology could be overcome.nnnCONCLUSIONnThe study has shown that phase coding in the human brain is a possible mechanism for information coding or transfer during visuo-motor tasks, while the phase-coded content about the gripping forces can be successfully extracted using the phase-demodulation approach. Since the methodology has proven to be appropriate for the case of this study it could also be used as an alternative approach for the development of BCIs for similar tasks.

Gripping-force identification using EEG and phase-demodulation approach.

In this paper we investigate the fuzzy identification of brain-code during simple gripping-force control tasks. Since the synchronized oscillatory activity and the phase dynamics between the brain areas are two important mechanisms in the brains function and information transfer, we decided to examine whether it is possible to extract the encoded information from the EEG signals using the phase-demodulation approach. The EEG was measured during the performance of different visuomotor tasks and the information we were trying to decode was the gripping force as applied by the subjects. The study revealed that it is possible, by using simple beta-rhythm filtering, phase demodulation, principal component analysis and a fuzzy model, to estimate the gripping-force response by using EEG signals as the inputs for the proposed model. The presented study has shown that even though EEG signals represent a superposition of all the active neurons, it is still possible to decode some information about the current activity of the brain centers. Furthermore, the cross-validation showed that the information about the gripping force is encoded in a very similar way for all the examined subjects. Thus, the phase shifts of the EEG signals seem to have a key role during activity and information transfer in the brain, while the phase-demodulation method proved to be a crucial step in the signal processing.

Sniffing-related motor cortical potential: topography and possible generators.

This study estimated the whole-scalp topography and possible generators of the cortical potential associated with volitional self-paced inspirations (sniffs). In 17 healthy subjects we recorded a 32-channel electroencephalogram (EEG) during sniffing, for comparison during finger flexions. We averaged the EEG with respect to movement onset, and performed current source density and principal component analysis on the grand averaged data. We identified an early negative sniffing-related cortical potential starting ∼1.5s before movement at the vertex, which, in its time-course and dipole orientation, closely resembled Bereitshaftspotential preceding finger flexions. Around the movement onset, its topography became unique with three negative current sources: one at the vertex, and two bilaterally over the fronto-temporal derivations. We conclude that sequential cortical activation in preparation for sniffing is similar to other volitional movements. The current sources at sniff onset at the vertex likely reflect somatotopic motor representation of the diaphragm, neck and intercostal muscles, whereas current sources over fronto-temporal derivations likely reflect the somatotopic representation of the orofacial muscles.

The electrophysiological correlates of the working memory subcomponents: evidence from high-density EEG and coherence analysis

Synchronization between prefrontal (executive) and posterior (association) cortices seems a plausible mechanism for temporary maintenance of information. However, while EEG studies reported involvement of (pre)frontal midline structures in synchronization, functional neuroimaging elucidated the importance of lateral prefrontal cortex (PFC) in working memory (WM). Verbal and spatial WM rely on lateralized subsystems (phonological loop and visuospatial sketchpad, respectively), yet only trends for hemispheric dissociation of networks supporting rehearsal of verbal and spatial information were identified by EEG. As oscillatory activity is WM load dependent, we applied an individually tailored submaximal load for verbal (V) and spatial (S) task to enhance synchronization in the relevant functional networks. To map these networks, we used high-density EEG and coherence analysis. Our results imply that the synchronized activity is limited to highly specialized areas that correspond well with the areas identified by functional neuroimaging. In both V and S task, two independent networks of theta synchronization involving dorsolateral PFC of each hemisphere were revealed.xa0In V task, left prefrontal and left parietal areas were functionally coupled in gamma frequencies. Theta synchronization thus provides the necessary interface for storage and manipulation of information, while left-lateralized gamma synchronization could represent the EEG correlate of the phonological loop.

Identification of Human Gripping-force Control from Electro-encephalographic Signals by Artificial Neural Networks

Abstract The exact mechanism of information transfer between different brain regions is still not known. The theory of binding tries to explain how different aspects of perception or motor action combine in the brain to form a unitary experience. The theory presumes that there is no specific center in the brain that would gather the information from all the other brain centers, governing senses, motion, etc., and then make the decision about the action. Instead, the centers bind together, when necessary, maybe through electromagnetic (EM) waves of specific frequency. Therefore, it is reasonable to assume that the information that is transferred between the brain centers is somehow coded in the electro-encephalographic (EEG) signals. The aim of this study was to explore whether it is possible to extract the information on brain activity from the EEG signals during visuomotor tracking task. In order to achieve the goal, artificial neural network (ANN) was used to predict the measured gripping-force from the EEG signal measurements and thus to show the correlation between EEG signals and motor activity. The ANN was first trained with raw EEG signals of all the measured electrodes as inputs and gripping-force as the output. However, the ANN could not be trained to perform the task successfully. If we presume that brain centers transmit and receive information through EM signals, as suggested by the binding theory, a simplified model of signal transmission in brain can be proposed. We propose a mathematical model of a human brain where the information between centers is transmitted as phase-modulated signal of certain carrier frequency. Demodulated signals were then used as the inputs for the ANN and the gripping-force signal was estimated on the output. The ANN could be trained to efficiently predict the gripping-force signal from the phase-demodulated EEG signals.

Identification of Gripping-Force Control from Electroencephalographic Signals

The exact mechanism of information transfer between different brain regions is still not known. The theory of binding tries to explain how different aspects of perception or motor action combine in the brain to form a unitary experience. The theory presumes that there is no specific center in the brain that would gather the information from all the other brain centers, governing senses, motion, etc., and then make the decision about the action. Instead, the centers bind together when necessary, maybe through electromagnetic (EM) waves of specific frequency. Therefore, it is reasonable to assume that the information that is transferred between the brain centers is somehow coded in the electroencephalographic (EEG) signals. The aim of this study was to explore whether it is possible to extract the information on brain activity from the EEG signals during visuomotor tracking task. In order to achieve the goal, artificial neural network (ANN) was used. The ANN was used to predict the measured gripping-force from the EEG signal measurements and thus to show the correlation between EEG signals and motor activity. The ANN was first trained with raw EEG signals of all the measured electrodes as inputs and gripping-force as the output. However, the ANN could not be trained to perform the task successfully. If we presume that brain centers transmit and receive information through EM signals, as suggested by the binding theory, a simplified model of signal transmission in brain can be proposed. We propose a computational model of a human brain where the information between centers is transmitted as phase-modulation of certain carrier frequency. Demodulated signals were then used as the inputs for the ANN and the gripping-force signal was used as the output. It was possible to train the network to efficiently calculate the gripping-force signal from the phase-demodulated EEG signals.

Using ANN on EEG signals to predict working memory task response

Many authors have shown that performing working-memory tasks causes an elevated neuronal activity in several areas of the human brain, which suggests information exchange between them. Since the information exchanged, encoded in brain waves is measurable by electroencephalography (EEG) it is reasonable to assume that it can be extracted with an appropriate method. In this paper we present a method for extracting the information using an artificial neural network (ANN), which we consider as a stimulusresponse model. The EEG was recorded from three subjects while they performed a modified Sternberg task that required them to respond to each trial with the answer true or false. The study revealed that a stimulus-response model can successfully be identified by observing phase-demodulated theta-band EEG signals 1 second prior to a subjects answer. The results showed that the model was able to predict the answers from the EEG signals with an average reliability of 75% for all three subjects. From this we concluded that stimulus-response model successfully observes the system states and consequently predicts the correct answer using the EEG signals as inputs.