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We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY(More)
Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding exact time series motifs in massive(More)
A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial(More)
Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes in user cognitive state, intent, and response to events are of increasing interest. Brain-computer interface (BCI) systems can make use of such knowledge to(More)
We first review the range of standalone and MATLAB-based software currently freely available to BCI researchers. We then discuss two MATLAB-centered solutions for realtime data streaming, the environments FieldTrip (Donders Institute, Nijmegen) and DataSuite (DataRiver, Producer, MatRiver) (Swartz Center, La Jolla). We illustrate the relative simplicity of(More)
Development of EEG-based brain computer interface (BCI) methods has largely focused on creating a communication channel for subjects with intact cognition but profound loss of motor control from stroke or neurodegenerative disease that allows such subjects to communicate by spelling out words on a personal computer. However, other important human(More)
UNLABELLED This paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG data. A novel method that aims to identify single-trial event-related potentials (ERPs) in time-frequency is proposed, and a robust classifier with feature clustering is developed to better(More)
The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing(More)
Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding time series motifs exactly in(More)