Least-squares deconvolution of evoked potentials and sequence optimization for multiple stimuli under low-jitter conditions

Bardy, Fabrice; Dillon, Harvey; Dun, Bram Van

doi:10.1016/j.clinph.2013.09.030

Cited by 22 publications

(32 citation statements)

References 17 publications

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“…The iterative procedure of the I-RSA method can derive into problems of instability under certain distributions of the jitter and stimulation rates, leading to worse AEP estimates in successive iterations. Instability is a common problem that is also present in methods based in deconvolution [these methods must define stimulation sequences whose frequency components are not close to zero to avoid noise amplification (Jewett et al, 2004;Ozdamar and Bohorquez, 2006;Bardy et al, 2014a)]. The instability sometimes observed in I-RSA is associated to the fact that I-RSA performs some kind of deconvolution in the time domain [like in the case of ADJAR (Woldorff, 1993)].…”

Section: Discussionmentioning

confidence: 98%

“…The ADJAR technique obtains the transient AEP iteratively in the time domain by convolving the AEP estimate in each iteration with the statistical ISI distribution of the stimulation sequence to estimate the separate effects of preceding and succeeding stimuli on the averaged response. This technique has been widely used in event-related potentials (ERPs) (Bekker et al, 2005;Wang et al, 2008), however, this method has been found difficult to implement (Talsma and Woldorff, 2005;Wang et al, 2006) and problems of instability have been reported by some authors (Bardy et al, 2014a). The QSD, CLAD, and LS methods present different approaches to deconvolve overlapping auditory responses evoked by low-jittered stimuli.…”

Section: Introductionmentioning

confidence: 98%

“…The jitter of a stimulation sequence measures the dispersion of the ISI compared with a periodical presentation of stimuli, with constant ISI. The most relevant methods used to obtain AEPs at high rates are maximum length sequences (MLS) (Eysholdt and Schreiner, 1982), ADJAR (Woldorff, 1993), quasiperiodic sequence deconvolution (QSD) (Jewett et al, 2004), continuous loop averaging deconvolution (CLAD) (Ozdamar et al, 2003a;Ozdamar et al, 2003b;Delgado and Ozdamar, 2004;Ozdamar and Bohorquez, 2006), least-squares (LS) deconvolution (Bardy et al, 2014a), and randomized stimulation and averaging (RSA) (Valderrama et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Auditory brainstem and middle latency responses recorded at fast rates with randomized stimulation

Valderrama

Torre

Álvarez

et al. 2014

The Journal of the Acoustical Society of America

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Randomized stimulation and averaging (RSA) allows auditory evoked potentials (AEPs) to be recorded at high stimulation rates. This method does not perform deconvolution and must therefore deal with interference derived from overlapping transient evoked responses. This paper analyzes the effects of this interference on auditory brainstem responses (ABRs) and middle latency responses (MLRs) recorded at rates of up to 300 and 125 Hz, respectively, with randomized stimulation sequences of a jitter both greater and shorter than the dominant period of the ABR/MLR components. Additionally, this paper presents an advanced approach for RSA [iterative-randomized stimulation and averaging (I-RSA)], which includes the removal of the interference associated with overlapping responses through an iterative process in the time domain. Experimental results show that (a) RSA can be efficiently used in the recording of AEPs when the jitter of the stimulation sequence is greater than the dominant period of the AEP components, and (b) I-RSA maintains all the advantages of RSA and is not constrained by the restriction of a minimum jitter. The significance of the results of this study is discussed.

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Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Auditory brainstem and middle latency responses recorded at fast rates with randomized stimulation

Valderrama

Torre

Álvarez

et al. 2014

The Journal of the Acoustical Society of America

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“…The conventional stimulation technique consists of the presentation of stimuli with a constant interstimulus interval (ISI) greater than the averaging window to avoid overlapping responses [4]. Other more advanced methods, such as maximum length sequences (MLS) [9], continuous loop averaging deconvolution (CLAD) [5,23], quasi periodic sequence deconvolution (QSD) [18], least-squares deconvolution (LS) [2,3], and randomized stimulation and averaging (RSA) [28], can also be implemented to obtain AER at high stimulation rates. The stimulation of the auditory system is typically performed by 0.1 ms duration clicks in rarefaction polarity to evoke a synchronous firing of a large number of neurons [13].…”

Section: Software Specificationsmentioning

confidence: 99%

A flexible and inexpensive high-performance auditory evoked response recording system appropriate for research purposes

Valderrama

Torre

Álvarez

et al. 2014

Biomedical Engineering / Biomedizinische Technik

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Recording auditory evoked responses (AER) is done not only in hospitals and clinics worldwide to detect hearing impairments and estimate hearing thresholds, but also in research centers to understand and model the mechanisms involved in the process of hearing. This paper describes a high-performance, flexible, and inexpensive AER recording system. A full description of the hardware and software modules that compose the AER recording system is provided. The performance of this system was evaluated by conducting five experiments with both real and artificially synthesized auditory brainstem response and middle latency response signals at different intensity levels and stimulation rates. The results indicate that the flexibility of the described system is appropriate to record AER signals under several recording conditions. The AER recording system described in this article is a flexible and inexpensive high-performance AER recording system. This recording system also incorporates a platform through which users are allowed to implement advanced signal processing methods. Moreover, its manufacturing cost is significantly lower than that of other commercially available alternatives. These advantages may prove useful in many research applications in audiology.

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“…It is usually implemented in the frequency domain to take the computational advantage of fast Fourier transform (FFT). To date, multiple related techniques have been introduced, leading to several new deconvolution algorithms, such as the randomized stimulation and averaging method proposed by Valderrama et al [13], least-squares deconvolution method by Bardy et al [14], and multi-rate steady-state averaging deconvolution method by Wang et al [15]. These methods have been found valuable for studies of transient AEPs and their roles in the generation of SSRs [16–18].…”

Section: Introductionmentioning

confidence: 99%

New metric for optimizing Continuous Loop Averaging Deconvolution (CLAD) sequences under the 1/f noise model

et al. 2017

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Continuous loop averaging deconvolution (CLAD) is one of the proven methods for recovering transient auditory evoked potentials (AEPs) in rapid stimulation paradigms, which requires an elaborated stimulus sequence design to attenuate impacts from noise in data. The present study aimed to develop a new metric in gauging a CLAD sequence in terms of noise gain factor (NGF), which has been proposed previously but with less effectiveness in the presence of pink (1/f) noise. We derived the new metric by explicitly introducing the 1/f model into the proposed time-continuous sequence. We selected several representative CLAD sequences to test their noise property on typical EEG recordings, as well as on five real CLAD electroencephalogram (EEG) recordings to retrieve the middle latency responses. We also demonstrated the merit of the new metric in generating and quantifying optimized sequences using a classic genetic algorithm. The new metric shows evident improvements in measuring actual noise gains at different frequencies, and better performance than the original NGF in various aspects. The new metric is a generalized NGF measurement that can better quantify the performance of a CLAD sequence, and provide a more efficient mean of generating CLAD sequences via the incorporation with optimization algorithms. The present study can facilitate the specific application of CLAD paradigm with desired sequences in the clinic.

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Least-squares deconvolution of evoked potentials and sequence optimization for multiple stimuli under low-jitter conditions

Cited by 22 publications

References 17 publications

Auditory brainstem and middle latency responses recorded at fast rates with randomized stimulation

Auditory brainstem and middle latency responses recorded at fast rates with randomized stimulation

A flexible and inexpensive high-performance auditory evoked response recording system appropriate for research purposes

New metric for optimizing Continuous Loop Averaging Deconvolution (CLAD) sequences under the 1/f noise model

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