Unsupervised wavelet-based spike sorting with dynamic codebook searching and replenishment

Chan, Hsiao‐Lung; Wu, Tony; Lee, Shih‐Tseng; Lin, Ming-An; He, Shau-Ming; Chao, Pei-Kuang; Tsai, Yu-Tai

doi:10.1016/j.neucom.2009.11.006

Cited by 20 publications

(10 citation statements)

References 24 publications

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“…Alternatively, principal component analysis (PCA) is a popular method used for feature extraction in spike sorting [16][17][18]. WT [19] also has emerged as a competitive feature extraction method [20][21][22][23].…”

Section: Feature Extraction: Diffusion Maps Vs Wavelet Transformationmentioning

confidence: 99%

Automatic spike sorting by unsupervised clustering with diffusion maps and silhouettes

et al. 2015

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Section: Feature Extraction: Diffusion Maps Vs Wavelet Transformationmentioning

confidence: 99%

Automatic spike sorting by unsupervised clustering with diffusion maps and silhouettes

et al. 2015

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“…Previous works (Chan et al 2010;Chuang et al 2012) have employed processing approaches based on temporal analysis of spikes. In Chan et al (2010), an unsupervised spike sorting method based on specific wavelet coefficients was implemented. The authors use a spike alignment technique based on multi-peak energy comparison (MPEC), and a dynamic codebook-based template-matching algorithm with a class-merging feature.…”

Section: Introductionmentioning

confidence: 99%

Multi-task learning for subthalamic nucleus identification in deep brain stimulation

Cardona

López

Orozco

2017

Int. J. Mach. Learn. & Cyber.

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Deep brain stimulation (DBS) of Subthalamic nucleus (STN) is the most successful treatment for advanced Parkinson's disease. Localization of the STN through Microelectrode recordings (MER) is a key step during the surgery. However, it is a complex task even for a skilled neurosurgeon. Different researchers have developed methodologies for processing and classification of MER signals to locate the STN. Previous works employ the classical paradigm of supervised classification, assuming independence between patients. The aim of this paper is to introduce a patientdependent learning scenario, where the predictive ability for STN identification at the level of a particular patient, can be used to improve the accuracy for STN identification in other patients. Our inspiration is the multi-task learning framework, that has been receiving increasing interest within the machine learning community in the last few years. To this end, we employ the multi-task Gaussian processes framework that exhibits state of the art performance in multi-task learning problems. In our context, we assume that each patient undergoing DBS is a different task, and we refer to the method as multi-patient learning. We show that the multipatient learning framework improves the accuracy in the identification of STN in a range from 4.1% to 7.7%, compared to the usual patient-independent setup, for two different datasets. Given that MER are non stationary and noisy signals. Traditional approaches in machine learning fail to recognize accurately the STN during DBS. By contrast in our proposed method, we properly exploit correlations between patients with similar diseases, obtaining an additional information. This information allows to improve the accuracy not only for locating STN for DBS but also for other biomedical signal classification problems.

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“…Improving the interpretation of the recording based on these factors is of great importance for cell identification in various applications such as brain computer interface (BCI) (Ortiz-Rosario and Adeli, 2013) and understanding and diagnosis of various neurological disorders (Florin et al, 2013). To that end, approaches have been proposed such as tetrode electrode arrangements (Gray et al, 1995), spectral transformations (Luczak and Narayanan, 2005), template matching (Kaneko et al, 1999), and various approaches using wavelet transform (WT) (Pavlov et al, 2007; Wiltschko et al, 2008; Shalchyan et al, 2012; Lai et al, 2011; Chan et al, 2010; Geng and Hu, 2012). …”

Section: Introductionmentioning

confidence: 99%

Wavelet methodology to improve single unit isolation in primary motor cortex cells

Ortiz-Rosario

Adeli

Buford

2015

Journal of Neuroscience Methods

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The proper isolation of action potentials recorded extracellularly from neural tissue is an active area of research in the fields of neuroscience and biomedical signal processing. This paper presents an isolation methodology for neural recordings using the wavelet transform (WT), a statistical thresholding scheme, and the principal component analysis (PCA) algorithm. The effectiveness of five different mother wavelets was investigated: biorthogonal, Daubachies, discrete Meyer, symmetric, and Coifman; along with three different wavelet coefficient thresholding schemes: fixed form threshold, Stein’s unbiased estimate of risk, and minimax; and two different thresholding rules: soft and hard thresholding. The signal quality was evaluated using three different statistical measures: mean-squared error, root-mean squared, and signal to noise ratio. The clustering quality was evaluated using two different statistical measures: isolation distance, and L-ratio. This research shows that the selection of the mother wavelet has a strong influence on the clustering and isolation of single unit neural activity, with the Daubachies 4 wavelet and minimax thresholding scheme performing the best.

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Unsupervised wavelet-based spike sorting with dynamic codebook searching and replenishment

Cited by 20 publications

References 24 publications

Automatic spike sorting by unsupervised clustering with diffusion maps and silhouettes

Automatic spike sorting by unsupervised clustering with diffusion maps and silhouettes

Multi-task learning for subthalamic nucleus identification in deep brain stimulation

Wavelet methodology to improve single unit isolation in primary motor cortex cells

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