The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Across neuroscience research, clinical diagnostics, and engineering applications in pain evaluation and treatment, there is a need for an objective measure of pain experience and detection when it occurs. This detector should be reliable in real-world settings using easily accessible, non-invasive data sources. We present a simple yet robust paradigm for decoding pain using neural and physiological...
Electroencephalography has been studied to understand various brain functions. These functions can be related to their frequency and spatial patterns. These properties are relatively unknown in infants. The first year of life includes stages of significant growth neurologically and behaviorally. The present study is aimed to investigate infant brain development using high-density EEG(124 channel)...
Parkinson's disease (PD) is a progressive, neurodegenerative disorder, characterized by hallmark motor symptoms. Deep brain stimulation (DBS) has been used to treat advanced PD successfully. Previous studies have found that the DBS also has an effect on the electrophysiological activity of the deep brain nucleus while alleviate the PD symptoms. Here, in an attempt to gain a greater understanding of...
In order to understand brain mechanisms and functionalities, neural probes with electrode arrays are incorporated into mice and Local Field Potentials (LFP) are recorded indicating the activities of groups of neurons. Next, the brain activity can be analyzed in terms of Current Source Density (CSD), which are computed via the LFP. In this paper, we propose the analysis of the somatosensory cortex...
Stereo-electroencephalographic (SEEG) depth electrodes were used to record neural activity from deep brain structures in this study. By localizing all the electrodes into the individual brain, we found that areas that are inside of central sulcus occurred obvious hand-movement-related modulation when the subjects were performing different hand motion tasks. Then, an asynchronous brain-computer interface...
In most Brain-Computer Interfaces (BCI) experimental paradigms based on Motor Imageries (MI), subjects perform continuous motor imagery (CMI), i.e. a repetitive and prolonged intention of movement, for a few seconds. To improve efficiency such as detecting faster a motor imagery, the purpose of this study is to show the difference between a discrete motor imagery (DMI), i.e. a single short MI, and...
Recently, SSVEP detection from EEG signals has attracted the interest of the research community, leading to a number of well-tailored methods, such as Canonical Correlation Analysis (CCA) and a number of variants. Despite their effectiveness, due to their strong dependence on the correct calculation of correlations, these methods may prove to be inadequate in front of potential deficiency in the number...
Motor imagery based BCIs are one of the most important BCI paradigms. Although it has been studied for a long time, the EEG features for kinetic information of motor imagery are still less known. In this paper, we explored EEG patterns of hand force motor imagery. Six subjects participated in this study, who were required to imagine clenching their hands with two different levels of force during the...
Working memory processing is central for higher-order cognitive functions. Although the ability to access and extract working memory load has been proven feasible, the temporal resolution is low and cross-task generalization is poor. In this study, EEG oscillatory activity was recorded from sixteen healthy subjects while they performed two versions of the visual n-back task. Observed effects in the...
This paper presents a model-based Field Programmable Gates Array (FPGA) design for real-time feature extraction of Electroencephalogram (EEG) signals, which can be used for brainwaves bands classification to track and detect mental status in Brain Computer Interface (BCI) applications and consciousness studies. An model-based design approach is used to implement Short-time Fourier Transform (STFT)...
A common problem in Brain-Machine Interface (BMI) is the variations in neural signals over time, leading to significant decrease in decoding performance if the decoder is not re-trained. However, frequent re-training is not practical in real use case. In our work, we found that a temporally more robust system may be achieved through the use of wavelet transform in feature extraction. We used wavelet...
This paper describes the importance of theta frequency oscillations through analysis in emotion regulation and in various oscillatory patterns with reappraising sad and fear stimuli. Previous research suggests that the processing of fear and sadness has different neural bases, and that dynamics in theta frequency is particularly sensitive to the processing of emotional stimuli. However, it remains...
To solve multiple comparisons problems in EEG/MEG analyses, cluster-based permutation test is possibly the most powerful approach, while it also inherits the advantage of well-controlled family-wise error rate from point-level permutation test. Because the cluster-level statistics used accumulate statistical power of all points in a cluster, cluster-based permutation test has a much higher sensitivity...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.