Introducing NPXLab 2010: A tool for the analysis and optimization of P300 based brain-computer interfaces

Bianchi, Luigi; Quitadamo, Lucia Rita; Abbafati, Manuel; Marciani, Maria Grazia; Saggio, Giovanni

doi:10.1109/isabel.2009.5373621

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…In order to improve the signal-to-noise ratio and to remove artifacts (e.g., network frequency interference, muscular artifacts, etc. ), a preprocessing procedure was applied to the raw EEG data by using the NPX Lab [ 32 ] software and some ad-hoc Matlab scripts. Raw scalp EEG data were band-filtered within 1–40 Hz and then the Independent Component Analysis (ICA) was used to remove the eye-blinks artefacts.…”

Section: Methodsmentioning

confidence: 99%

Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

Kong

Lin

Babiloni

et al. 2015

Sensors

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Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG) signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain’s ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC) in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers’ fatigue levels, and as reference work for future studies.

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Section: Methodsmentioning

confidence: 99%

Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

Kong

Lin

Babiloni

et al. 2015

Sensors

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“…: 1.9.8.314; Bianchi et al, 2009). Data were bandpass filtered between 1 Hz and 25 Hz and processed with Independent Component Analysis (ICA).…”

Section: Methodsmentioning

confidence: 98%

Cognitive Processing in Non-Communicative Patients: What Can Event-Related Potentials Tell Us?

Lugo

Quitadamo

Bianchi

et al. 2016

Front. Hum. Neurosci.

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Event-related potentials (ERP) have been proposed to improve the differential diagnosis of non-responsive patients. We investigated the potential of the P300 as a reliable marker of conscious processing in patients with locked-in syndrome (LIS). Eleven chronic LIS patients and 10 healthy subjects (HS) listened to a complex-tone auditory oddball paradigm, first in a passive condition (listen to the sounds) and then in an active condition (counting the deviant tones). Seven out of nine HS displayed a P300 waveform in the passive condition and all in the active condition. HS showed statistically significant changes in peak and area amplitude between conditions. Three out of seven LIS patients showed the P3 waveform in the passive condition and five of seven in the active condition. No changes in peak amplitude and only a significant difference at one electrode in area amplitude were observed in this group between conditions. We conclude that, in spite of keeping full consciousness and intact or nearly intact cortical functions, compared to HS, LIS patients present less reliable results when testing with ERP, specifically in the passive condition. We thus strongly recommend applying ERP paradigms in an active condition when evaluating consciousness in non-responsive patients.

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“…Functions to import/export into/from EEGLAB an “.npx/.set” file into a “.set/.npx” ( Bianchi et al, 2007 ). “Npx” is the native file format of the NPXLab suite ( Bianchi et al, 2009 ), a framework for the analysis of EEG and brain-computer interface data.…”

Section: Methodsmentioning

confidence: 99%

EPINETLAB: A Software for Seizure-Onset Zone Identification From Intracranial EEG Signal in Epilepsy

Quitadamo

Foley

Mai³

et al. 2018

Front. Neuroinform.

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The pre-operative workup of patients with drug-resistant epilepsy requires in some candidates the identification from intracranial EEG (iEEG) of the seizure-onset zone (SOZ), defined as the area responsible of the generation of the seizure and therefore candidate for resection. High-frequency oscillations (HFOs) contained in the iEEG signal have been proposed as biomarker of the SOZ. Their visual identification is a very onerous process and an automated detection tool could be an extremely valuable aid for clinicians, reducing operator-dependent bias, and computational time. In this manuscript, we present the EPINETLAB software, developed as a collection of routines integrated in the EEGLAB framework that aim to provide clinicians with a structured analysis pipeline for HFOs detection and SOZ identification. The tool implements an analysis strategy developed by our group and underwent a preliminary clinical validation that identifies the HFOs area by extracting the statistical properties of HFOs signal and that provides useful information for a topographic characterization of the relationship between clinically defined SOZ and HFO area. Additional functionalities such as inspection of spectral properties of ictal iEEG data and import and analysis of source-space magnetoencephalographic (MEG) data were also included. EPINETLAB was developed with user-friendliness in mind to support clinicians in the identification and quantitative assessment of HFOs in iEEG and source space MEG data and aid the evaluation of the SOZ for pre-surgical assessment.

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Introducing NPXLab 2010: A tool for the analysis and optimization of P300 based brain-computer interfaces

Cited by 10 publications

References 13 publications

Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

Cognitive Processing in Non-Communicative Patients: What Can Event-Related Potentials Tell Us?

EPINETLAB: A Software for Seizure-Onset Zone Identification From Intracranial EEG Signal in Epilepsy

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