CARE-rCortex: A Matlab toolbox for the analysis of CArdio-REspiratory-related activity in the Cortex.

BACKGROUND: Although cardio-respiratory (CR) system is generally controlled by the autonomic nervous system, interactions between the cortex and these primary functions are receiving an increasing interest in neurosciences. NEW METHOD: In general, the timing of such internally paced events (e.g. heartbeats or respiratory cycles) may display a large variability. For the analysis of such CR event-related EEG potentials, a baseline must be correctly associated to each cycle of detected events. The open-source toolbox CARE-rCortex provides an easy-to-use interface to detect CR events, define baselines, and analyse in time-frequency (TF) domain the CR-based EEG potentials. RESULTS: CARE-rCortex provides some practical tools to detect and validate these CR events. Users can define baselines time-locked to a phase of respiratory or heart cycle. A statistical test has also been integrated to highlight significant points of the TF maps with respect to the baseline. We illustrate the use of CARE-rCortex with the analysis of two real cardio-respiratory datasets. COMPARISON WITH EXISTING METHODS: Compared to other open-source toolboxes, CARE-rCortex allows users to automatically detect CR events, to define and check baselines for each detected event. Different baseline normalizations can be used in the TF analysis of EEG epochs. CONCLUSIONS: The analysis of CR-related EEG activities could provide valuable information about cognitive or pathological brain states. CARE-rCortex runs in Matlab as a plug-in of the EEGLAB software, and it is publicly available at https://github.com/FannyGrosselin/CARE-rCortex.

Surrogate-Based Artifact Removal From Single-Channel EEG.

OBJECTIVE: the recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer interfacing. These recordings are, however, contaminated by many sources of noise degrading the signals of interest, thus compromising the interpretation of the underlying brain state. In this paper, we propose a new data-driven algorithm to effectively remove ocular and muscular artifacts from single-channel EEG: the surrogate-based artifact removal (SuBAR). METHODS: by means of the time-frequency analysis of surrogate data, our approach is able to identify and filter automatically ocular and muscular artifacts embedded in single-channel EEG. RESULTS: in a comparative study using artificially contaminated EEG signals, the efficacy of the algorithm in terms of noise removal and signal distortion was superior to other traditionally-employed single-channel EEG denoizing techniques: wavelet thresholding and the canonical correlation analysis combined with an advanced version of the empirical mode decomposition. Even in the presence of mild and severe artifacts, our artifact removal method provides a relative error 4 to 5 times lower than traditional techniques. SIGNIFICANCE: in view of these results, the SuBAR method is a promising solution for mobile environments, such as ambulatory healthcare systems, sleep stage scoring, or anesthesia monitoring, where very few EEG channels or even a single channel is available.