Recursive independent component analysis for online blind source separation

Akhtar, Muhammad Tahir; Jung, Tzyy‐Ping; Makeig, Scott; Cauwenberghs, Gert

doi:10.1109/iscas.2012.6271896

Cited by 41 publications

(36 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm offers the convergence properties of batch ICA with incremental updates of a form similar to natural gradient (NG) on--line information maximization (Infomax). We found significant gains in convergence rate over on--line natural gradient ICA [Akhtar, et al, 2012;Hsu et al, under review].…”

Section: Counting Countingmentioning

confidence: 73%

Concurrent EEG And NIRS Tomographic Imaging Based on Wearable Electro-Optodes

Duann¹,

Jung²,

Chiou³

2014

Self Cite

View full text Add to dashboard Cite

Section: Counting Countingmentioning

confidence: 73%

Concurrent EEG And NIRS Tomographic Imaging Based on Wearable Electro-Optodes

Duann¹,

Jung²,

Chiou³

2014

Self Cite

View full text Add to dashboard Cite

“…As a result, microcircuit-appropriate methods for online system identification, streaming clustering and factor analysis (for online updating low-rank estimates embedded in dynamical systems like those in Buesing et al, 2014; Pfau et al, 2013), and change-point detection algorithms to identify rapid shifts in animal brain state (for example, those seen during sharp wave ripples as compared to during theta oscillations in hippocampus (Buzsaki, 2006) will all be useful. Various relevant streaming clustering and factor analytic approaches have already been developed (Akhtar et al, 2012; Mairal et al, 2010; O’Callaghan et al, 2002). Change-point identification methods have also been developed for spiking neural data (Pillow et al, 2011), and could help identify large state changes in system dynamics in such a way that several state-appropriate models could be fit and switched among.…”

Section: Online Experiments Design For Neural Microcircuitsmentioning

confidence: 99%

Closed-Loop and Activity-Guided Optogenetic Control

Grosenick

Marshel

Deisseroth

2015

Neuron

336

322

View full text Add to dashboard Cite

Advances in optical manipulation and observation of neural activity have set the stage for widespread implementation of closed-loop and activity-guided optical control of neural circuit dynamics. Closing the loop optogenetically (i.e., basing optogenetic stimulation on simultaneously observed dynamics in a principled way) is a powerful strategy for causal investigation of neural circuitry. In particular, observing and feeding back the effects of circuit interventions on physiologically relevant timescales is valuable for directly testing whether inferred models of dynamics, connectivity, and causation are accurate in vivo. Here we highlight technical and theoretical foundations as well as recent advances and opportunities in this area, and we review in detail the known caveats and limitations of optogenetic experimentation in the context of addressing these challenges with closed-loop optogenetic control in behaving animals.

show abstract

“…After EEG raw data are acquired from each channel, a whitening transformation is performed to effectively create an uncorrelated vector Z in order to accelerate the training processing. The vector Z is used to find the independent component Y and update the unmixing weight W in ORICA training [6].…”

Section: A Orica Enginementioning

confidence: 99%

“…In order to produce a better analysis of the HRV spectral estimation, the Lomb periodogram was adopted for estimating the power spectrum of the unevenly sampled signals. The Lomb periodogram is shown in (6). …”

Section: B Hrv Enginementioning

confidence: 99%

A highly integrated biomedical multiprocessor SoC design for a wireless bedside monitoring system

Lin

Liao

Fang³

2014

2014 IEEE International Symposium on Circuits and Systems (ISCAS)

View full text Add to dashboard Cite

This paper presents a highly integrated multiprocessor system-on-chip (SoC) design, enabling real-time processing of multi-biomedical signals in a wireless bedside monitoring system. This system includes a real-time online recursive independent component analysis (ORICA) processor to automatically remove brain electroencephalogram (EEG) artifacts signals, a heart rate variability (HRV) analysis processor for monitoring electrocardiogram (ECG) signals, and a basic biomedical signal processor for monitoring common physiological data such as oxygen saturation, blood pressure, or body temperature. The multiprocessor chip is fabricated using TSMC 90 nm CMOS technology, and occupies a core area of 1,600 x 1,600 um 2 . Simulated power consumption is 15.89 mW under the conditions of 1.0V core supply voltage and 64 MHz clock operation frequency.

show abstract

Recursive independent component analysis for online blind source separation

Cited by 41 publications

References 14 publications

Concurrent EEG And NIRS Tomographic Imaging Based on Wearable Electro-Optodes

Concurrent EEG And NIRS Tomographic Imaging Based on Wearable Electro-Optodes

Closed-Loop and Activity-Guided Optogenetic Control

A highly integrated biomedical multiprocessor SoC design for a wireless bedside monitoring system

Contact Info

Product

Resources

About