We support boolean queries, use +,-,<,>,~,* to alter the weighting of terms
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on December 12,2025. Multimodal dataset for emotion analysis using EEG, Physiological and Video Signals of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection was used, utilizing retrieval by affective tags from the last.fm website, video highlight detection and an online assessment tool. The dataset is made publicly available and other researchers are encouraged to use it for testing their own affective state estimation methods.
EEG motor activity data sets used for Brain Computer Interface research project in Matlab MAT format. * Dataset 1 - 1D motion: This subject is a 21 year old, right handed male with no known medical conditions. The EEG consists of actual random movements of left and right hand recorded with eyes closed. Each row represents one electrode. The order of the electrodes is FP1 FP2 F3 F4 C3 C4 P3 P4 O1 O2 F7 F8 T3 T4 T5 T6 FZ CZ PZ. The recording was done at 500Hz using Neurofax EEG System which uses a daisy chain montage. The data was exported with a common reference using Eemagine EEG. AC Lines in this country work at 50 Hz. This info is also included in the MAT file. * Dataset 2 - 2D motion: This subject is a 21 year old, right handed male with no known medical conditions. The EEG consists of actual random movements of left and right hand recorded with eyes closed. Each row represents one electrode. The order of the electrodes is FP1 FP2 F3 F4 C3 C4 P3 P4 O1 O2 F7 F8 T3 T4 T5 T6 FZ CZ PZ. The recording was done at 500Hz using Neurofax EEG System which uses a daisy chain montage. The data was exported with a common reference using Eemagine EEG. AC Lines in this country work at 50 Hz. This data consists of the following movements # Three trials left hand forward movement # Three trials left hand backward movement # Three trials left hand forward movement # Three trials left hand forward movement # 1 trial imagined left hand forward movement # 1 trial imagined left hand backward movement # 1 trial imagined right hand forward movement # 1 trial imagined right hand backward movement # 1 trial left leg movement # 1 trial right leg movement
A portal containing EEG datasets (in MATLAB format) and the MATLAB software that were used to produce the results in the paper named in the title of this resource. The files published can also be used as a basis for individual research on P300-based brain-computer interfaces. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25 bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested.
Software package for normalizing and analyzing tiling arrays and ChIP-chip data. It is the R-version of a MAT program.
THIS RESOURCE IS NO LONGER IN SERVICE, documented November 23, 2020; EEG data set, source code, and results from 7500 signal pairs from 5 epilepsy patients analyzed in the manuscript, Andrzejak RG, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E, 86, 046206, 2012. All Matlab source codes are included in the file ASR_Sources_2012_10_16.zip. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control.
Data set from a large study to examine EEG correlates of genetic predisposition to alcoholism. It contains measurements from 64 electrodes placed on the scalp sampled at 256 Hz (3.9-msec epoch) for 1 second. There were two groups of subjects: alcoholic and control. Each subject was exposed to either a single stimulus (S1) or to two stimuli (S1 and S2) which were pictures of objects chosen from the 1980 Snodgrass and Vanderwart picture set. When two stimuli were shown, they were presented in either a matched condition where S1 was identical to S2 or in a non-matched condition where S1 differed from S2. There were 122 subjects and each subject completed 120 trials where different stimuli were shown. The electrode positions were located at standard sites (Standard Electrode Position Nomenclature, American Electroencephalographic Association 1990). Zhang et al. (1995) describes in detail the data collection process. There are three versions of the EEG data set. * The Small Data Set (smni97_eeg_data.tar.gz) contains data for the 2 subjects, alcoholic a_co2a0000364 and control c_co2c0000337. For each of the 3 matching paradigms, c_1 (one presentation only), c_m (match to previous presentation) and c_n (no-match to previous presentation), 10 runs are shown. * The Large Data Set (SMNI_CMI_TRAIN.tar.gz and SMNI_CMI_TEST.tar.gz) contains data for 10 alcoholic and 10 control subjects, with 10 runs per subject per paradigm. The test data used the same 10 alcoholic and 10 control subjects as with the training data, but with 10 out-of-sample runs per subject per paradigm. * The Full Data Set contains all 120 trials for 122 subjects. The entire set of data is about 700 MBytes.
5 EEG, ERP and single cell recordings data sets where each file corresponds to the recording on a different subject in the left occipital electrode (O1), with linked earlobes reference. Each file contains several artifact-free trials, each of them containing 512 data points (256 pre- and 256 post-stimulation) stored with a sampling frequency of 250 Hz. Trials are stored consecutively in a 1 column file. Data was pre-filtered in the range 0.1-70Hz. All trials correspond to target stimulation with an oddball paradigm. STAR R based Data Sets Used * Dataset # 1: Human single-cell recording * Dataset # 2: Simulated extracellular recordings * Dataset # 3: EEG signals from rats * Dataset # 4: Pattern visual evoked potentials. * Dataset # 5: Tonic-clonic (Grand Mal) seizures.
Five data sets containing quasi-stationary, artifact-free EEG signals both in normal subjects and epileptic patients were put in the web by Ralph Andrzejak from the Epilepsy center in Bonn, Germany. Each data set contains 100 single channel EEG segments of 23.6 sec duration.
Software that calls aberrations for array CGH data using a six state mixture model and several biological concepts. It is written in R.
THIS RESOURCE IS NO LONGER IN SERVICE, documented August 17, 2016. C#.NET 4.0 application that interfaces with the Neural Maestro class library to perform Classical and Bayesian logistic polynomial regression analysis for multiple trial and experimental neuroscience / electrophysiological datasets. The idea is to build a neural feature extractor to plug into a neural recognition network based on recordings from patch clamps and/or brain computer interfaces. Frequency based feature sets are collected from the simulation of neuronal cell assemblies and examined for inclusion in combinatorial regressions using the Neural Maestro API. Regressions are evaluated based on different metrics. The application interfaces with a R and MATLAB API and produces a compendium to be published on CRAN.
BRI investigators study the molecular and genetic mechanisms which underlie some of the most devastating chronic neurological disorders, and conduct clinical trials for new innovative therapies. Neurological studies that are currently studied include Amyotrophic lateral sclerosis (ALS) or Lou Gehrig's Disease, Multiple Sclerosis, and Parkinson's Disease.
A web-based gene list enrichment analysis tool that provides various types of visualization summaries of collective functions of gene lists. It includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes / proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries.
Manually curated, comprehensive repository of clusters of differentiation (CDs) which are a) defined as distinct oligosaccharide sequences as part of either glycoproteins and/or glycosphingolipids and b) defined as proteins which have carbohydrate recognition sites (CRDs) or as carbohydrate binding lectins. The data base is generated by exhaustive search of literature and other online data banks related to carbohydrates and proteins. This data bank is the beginning of an effort to provide concise, relevant information of carbohydrate-related CDs in a user- friendly manner. For users convenience the data bank under menu browse of GlycoCD is arranged in two section namely carbohydrate recognition CDs (CRD CD) and glycan CD. The carbohydrate recognition CD part is the collection of proteins which recognize glycan structures by means of the CRDs. Glycan CD is the part in which CDs are summarized which characterize specific oligosaccharide structures. The GlycoCD databank has been developed with the aim to assist the immunologist, cell biologist as well as the clinician who wants to keep up with the present knowledge in this field of glycobiology.
Service that calculates and displays the main fragments (Band C-, Z- and Y-, A- and X-ions) of oligosaccharides that should occur in MS-spectra. The extended ASCII nomenclature as recommended by IUPAC is used to input the sequence of complex oligosaccharides. However, some additional input rules have to be fulfilled. In case only the topology and composition of the oligosaccharide is known, a simpler way to input carbohydrate sequences is possible. Since the hydroxyl groups of synthetic carbohydrates are often the are protected they have included a way to indicate if sugar residue are persubstituted. Please have a look at the examples of valid input structures.
Service that searches carbohydrate structures for motifs commonly used for carbohydrate classification, like N- and O-glycan cores, Lewis antigens, etc. Note: Sumo is currently under construction. Motif searches are a frequently used tool in proteomics. For carbohydrate structures, there are also many motifs classified in the literature, e.g. the Lewis antigens or the diverse O-glycan core structures. Sumo is a tool to locate such motifs in a carbohydrate structure given in LINUCS or in IUPAC nomenclature., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Service that directly converts the commonly used extended representation of complex carbohydrates into the preferred canonical description or into its inverted form. Input: A structure using the extended, non-graphic nomenclature (in ASCII writing) to describe complex carbohydrates as recommended by IUPAC. Output: A linear, unique notation. The source code (written in C), will be distributed so that software developers can easily implement their algorithm within their own application. LINUCS was chosen to fulfill to following conditions: * Input of extended, non-graphic nomenclature to describe carbohydrate structures. * Resulting linear code is closely related to notations and abbreviations recommended by IUPAC. * Number of additional rules to define the priority of the branches is low * Extended nomenclature of complex carbohydrates contains all information to define the hierarchy. * LINUCS is applicable to all types of carbohydrates (macrocyclic system are currently not implemented) . * Remaining unassigned linkage information are tolerated
Server that predicts N-Glycosylation sites in human proteins using artificial neural networks that examine the sequence context of Asn-Xaa-Ser/Thr sequons. NetNGlyc 1.0 is also available as a stand-alone software package, with the same functionality as the service above. Ready-to-ship packages exist for the most common UNIX platforms.
Service dedicated to statistically analyze the sequences around glycosylation sites. Glycosylation belongs to the most common and most important co- and postranslational modifications of proteins. Since it is often difficult to determine which potential glycosylation sites are in fact glycosylated, there is only few data available about glycoproteins. Sources from which such data can be retrieved are SwissProt and the Protein Data Bank (PDB). Data from the PDB is obtained using pdb2linucs and updated weekly. GlySeq is dedicated to statistically analyze these sequences, especially the areas around glycosylation sites.
Service that performs a statistical analysis of carbohydrate torsion angles derived from the Protein Data Bank. Such as protein conformation can be described by the backbone torsion angles, a carbohydrate structure is mainly characterised by its linkage torsions. With the aid of pdb2linucs, a dataset of carbohydrate torsion angles was derived from from carbohydrate structures found in the PDB. This weekly updated dataset contains, besides linkage torsions, also ring torsions, omega torsions, N-acetyle group torsions and sidechain torsions of Asn residues involved in Glycan bonds. It can be queried by GlyTorsion.
Service to generate statistics about the amino acids present in the vicinity of carbohydrate residues. Besides the amino acids in sequential neighborhood of glycosylation sites (analysed by GlySeq), those in the spatial vicinity of carbohydrate residues determine the characteristics of glycoproteins. The latter ones are of special interest for the examination of carbohydrate-binding proteins. Since carbohydrate moieties are not covalently bound in these cases, sequence analysis comparable to that for glycosylation sites is not possible there. GlyVicinity performs statistical analyses on the types of amino acids around carbohydrate chains and on the atoms forming the closest contacts between protein and carbohydrate residues. Results are based on weekly updated datasets derived from the Protein Data Bank (PDB).