scholarly journals Frequency-Domain Objective Response Detection Techniques Applied to Evoked Potentials: A Review

10.5772/36356 ◽  
2012 ◽  
Author(s):  
Danilo Barbosa ◽  
Antonio Mauricio Ferreira Leite Miranda de S ◽  
Antonio Fernando Catelli Infantosi
Keyword(s):  
Evoked Potentials ◽  
Frequency Domain ◽  
Objective Response ◽  
Detection Techniques ◽  
Response Detection

scholarly journals Using Objective Response Detection techniques for detecting the tibial somatosensory evoked response with different stimulation rates

2011 ◽  
Vol 195 (2) ◽  
pp. 255-260 ◽  
Author(s):  
Danilo Barbosa Melges ◽  
Antonio Fernando Catelli Infantosi ◽  
Antonio Mauricio Ferreira Leite Miranda de Sá
Keyword(s):  
Objective Response ◽  
Evoked Response ◽  
Detection Techniques ◽  
Somatosensory Evoked Response ◽  
Response Detection

Objective Response Detection in the Frequency Domain: Comparison of Several q-Sample Tests

1998 ◽  
Vol 4 (1) ◽  
pp. 2-11 ◽  
Author(s):  
Ekkehard Stürzebecher ◽  
Mario Cebulla ◽  
K.-D. Wernecke
Keyword(s):  
Frequency Domain ◽  
Objective Response ◽  
Response Detection

The Detection of Infant Cortical Auditory Evoked Potentials (CAEPs) Using Statistical and Visual Detection Techniques

2010 ◽  
Vol 21 (05) ◽  
pp. 347-356 ◽  
Author(s):  
Lyndal Carter ◽  
Maryanne Golding ◽  
Harvey Dillon ◽  
John Seymour
Keyword(s):  
Evoked Potentials ◽  
Repeated Measures ◽  
Auditory Evoked Potentials ◽  
Sensitivity Index ◽  
Screening Programs ◽  
Hotelling's T2 ◽  
Hotelling’S T2 ◽  
Response Detection ◽  
Cortical Auditory Evoked Potentials ◽  
Experienced Examiner

Background: With the advent of newborn hearing screening programs, the need to verify the fit of hearing aids in young infants has increased. The recording of cortical auditory evoked potentials (CAEPs) for this purpose is quite feasible, but rapid developmental changes that affect response morphology and the presence of electrophysiological noise can make subjective response detection challenging. Purpose: The purpose of this study was to investigate the effectiveness of an automated statistic versus experienced examiners in detecting the presence of infant CAEPs when stimuli were present and reporting the absence of CAEPs when no stimuli were present. Research Design: A repeated-measures design was used where infant-generated CAEPs were interpreted by examiners and an automated statistic. Study Sample: There were nine male and five female infants (mean age, 12 mo; SD, 3.4) who completed behavioral and electrophysiological testing using speech-based stimuli. Data Collection and Analysis: In total, 87 infant CAEPs were recorded to three sensation levels, 10, 20 and 30 dB relative to the behavioral thresholds and to nonstimulus trials. Three examiners were presented with these responses: (1) “in series,” where waveforms were presented in order of decreasing stimulus presentation levels, and (2) “nonseries,” where waveforms were randomized completely and presented as independent waveforms. The examiners were given no information about the stimulus levels and were asked to determine whether responses to auditory stimulation could be observed and their degree of certainty in making their decision. Data from the CAEP responses were also converted to multiple dependent variables and analyzed using Hotelling's T2. Results from both methods of response detection were analyzed using a repeated measures ANOVA (analysis of variance) and parameters of signal detection theory known as d-prime (d′) and the area under the receiver operating characteristic (ROC) curve. Results: Results showed that as the stimulus level increased, the sensitivity index, d′, increased for both methods of response detection, but neither reached the maximum possible d′ value with a sensation level of 30 dB. The examiners with the greatest experience and Hotelling's T2 were equally sensitive in differentiating the CAEP from noise. Conclusions: Hotelling's T2 appears to detect CAEPs from normal hearing infants at a rate equal to that of an experienced examiner. A clinical instrument that applies Hotelling's T2 on-line, so that the likelihood of response detection can be assessed objectively, should be of particular benefit to the novice or less experienced examiner.

Analyzing Steady-State Visual Evoked Potentials for Effective User Response Detection for Brain-Computer Interfaces

2013 ◽  
Author(s):  
Pouria Riyahi ◽  
Azim Eskandarian
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Time Windows ◽  
Time Step ◽  
Response Detection ◽  
Source Signals ◽  
Visual Evoked

This article evaluates an M-order Adaptive Kalman filter analysis on Steady-State Visual Evoked Potentials (SSVEPs). This model is based on finding the original brain source signals from their combined observed EEG signals. At each time step, observed brain signals are filtered according to their ideal reference signals measured from 10, 11, 12 and 13 Hz LED stimuli. SSVEP response detection is based on maximum Signal to Noise Ratio (SNR) of the brain source signals. In each test, the average system accuracy is calculated with and without overlapped time-windows along with system Information Transfer Rate (ITR). The overall system accuracy and ITR are showing promising level of SSVEP detection for future online BCI systems.

Sign in / Sign up

Export Citation Format

Share Document