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Our research team has previously used four Electroencephalography (EEG) leads to successfully detect and predict Freezing of Gait (FOG) in Parkinson's disease (PD). However, it remained to be determined whether these four sensor locations that were arbitrarily chosen based on their role in motor control are indeed the most optimal for FOG detection. The aim of this study was therefore to determine...
Gait Initiation Failure (GIF) is one of the most disabling gait disturbances seen in advanced Parkinson's disease (PD). Gait Initiation is a complex motor task that requires motor and cognitive processing to enable the correct selection, timing and scaling of movement. Failure to initiate the first step often precipitates falls and leads to significant morbidity. However, the brain mechanisms underlying...
Analyzing brain signals plays vital role in diagnosis and treatment of brain disorders. Brain signals obtained from electrodes of Electroencephalogram (EEG) are linear mixture of evoked potentials (EVP) of some number of neurons. Original signals have to be separated from linear mixture of source signals. Earlier work on disabled EEG signals considered analyzing electrode/sensor signals rather than...
Alzheimer's Disease (AD) and its preliminary stage - Mild Cognitive Impairment (MCI) - are the most widespread neurodegenerative disorders, and their investigation remains an open challenge. ElectroEncephalography (EEG) appears as a non-invasive and repeatable technique to diagnose brain abnormalities. Despite technical advances, the analysis of EEG spectra is usually carried out by experts that must...
The combination of repetitive transcranial magnetic stimulation (rTMS) and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. Using pre-treatment cordance, a relatively new quantitative EEG method combining complementary information from absolute and relative power of EEG spectra, 55 major depression disorder (MDD) subjects were classified into...
A wearable wireless general purpose bio-signal acquisition prototype system is presented in this paper. Three types of bio-signals could be acquired by the system then wirelessly transmitted to the computer for real-time processing. The prototype experimental results show that the system could acquire electrocardiogram (ECG), surface-electromyography (s-EMG) and electroencephalogram (EEG) for home...
Visually evoked potential (VEP) is an electrical signal generated by the brain (Occipital Cortex) in response to a visual stimuli. These responses are recorded non-invasively by placing the surface electrodes at the scalp, and observed as a reading on an electroencephalogram (EEG). This generated potential is smaller in amplitude compared to the EEG signal, which is in the range of 1 to 20µV compared...
In the present work, we used the brain electroencephalografic activity as an alternative means to identify individuals. 50 healthy subjects participated to the study and 56 EEG signals were recorded through a high-density cap during one minute of resting state either with eyes open and eyes closed. By computing the power spectrum density (PSD) on segments of 10 seconds, we obtained a feature vector...
Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebellar ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms...
The analysis of EEG signals plays an important role in a wide range of applications, such as sleep studies, seizure detection and hypnosis processing. Changes in different EEG frequencies have already been reported in association with hypnosis; however, it is difficult to compare different studies with each other because of methodological differences as well as different criteria when selecting subjects...
This article proposes a method to visualize information flows in the brain stemming from specific components making up electroencephalograms (EEGs). This method combines multidimensional directed information analysis, which is a method used in causality analysis of multidimensional time series, with independent component analysis to extract only directed information related to specific EEG components...
To explore the cross-information in multi-modal data, several multivariate data fusion techniques have been proposed. Partial least square (PLS) has great potential for neuroimaging studies. However, when performing group analysis with PLS, the presence of inter-subject variability makes the conventional technique of simply pooling data from different subjects problematic. To circumvent this issue,...
Electroencephalography (EEG) signal between normal and special children is slightly different. Different types of special children will generate different shape of EEG patterns depend on their neurological function. This paper demonstrates the classification of EEG signal for special children: to determine and to classify level and pattern of EEG signal for autism and Down syndrome children. EEG signal...
Undoubtedly, research and development activities in higher education institutions must have scientific, technological, social and economical impact in the surroundings. However, since the researcher's productivity in Mexico is measured by the number of publications, the results seldom conclude in a true technological development. This paper describes the result of an initiative between Morelia Institute...
In this paper, we have studied electroencephalogram (EEG) activity of schizophrenia patients, in resting eyes closed condition, with detrended fluctuation analysis (DFA). The DFA gives information about scaling and long-range correlations in time series. We computed DFA exponents from 30 scalp locations of 18 male neuroleptic-naïve, recent-onset schizophrenia (NRS) subjects and 15 healthy male control...
Convulsions represent a characteristic signal of neurological disease in the newborn period. Single-channel EEG is a convenient tool for continuous evaluation of neonatal convulsions and gives valuable prognostic information on neurological recovery. Among various abnormal EEG waveforms during convulsions, burst suppression (BS) pattern is distinctive and usually indicates an urgent state that therapeutic...
This study presents a preliminary analysis of the relationship between electroencephalographic (EEG) and electrocorticographic (ECoG) event-related potentials (ERPs) recorded from from a single patient using a brain-computer interface (BCI) speller. The patient had medically intractable epilepsy and underwent temporary placement of an intracranial ECoG grid electrode array to localize seizure foci...
An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose. The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before,...
The discovery of mirror neuron system in the macaque study in the 1990s explored a new way to investigate motor imitation. By using the electrocorticographic (ECoG) with high resolution in both spatial and temporal domains, this paper studies brain function during both observation and execution of a simple finger tapping task. Four epilepsy patients were asked to watch simple finger tapping video...
The study of Artificial Neural Networks (ANN) has been fascinating over the years and its development has strongly grown in recent years. The neural networks methods have become to be increasingly convincing for solving complex problems, through artificial intelligence. In particular, this work, focused on the development of an artificial neural network for identifying diseases: Parkinson's, Huntington's...
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