Spike sorting using hidden markov models

In this paper, hidden Markov models (HMM) is studied for spike sorting. We notice that HMM state sequences have capability to represent spikes precisely and concisely. We build a HMM for spikes, where HMM states respect spike significant shape variations. Four shape variations are introduced: silence, going up, going down and peak. They constitute every spike with an underlying probabilistic dependence that is modelled by HMM. Based on this representation, spikes sorting becomes a classification problem of compact HMM state sequences. In addition, we enhance the method by defining HMM on extracted Cepstrum features, which improves the accuracy of spike sorting. Simulation results demonstrate the effectiveness of the proposed method as well as the efficiency.

Neuron’s spikes noise level classification using hidden markov models

Considering that the uncertainty noise produced the decline in the quality of collected neural signal, this paper proposes a signal quality assessment method for neural signal. The method makes an automated measure to detect the noise levels in neural signal. Hidden Markov Models were used to build a classification model that classifies the neural spikes based on the noise level associated with the signal. This neural quality assessment measure will help doctors and researchers to focus on the patterns in the signal that have high signal to noise ratio and carry more information.

Noise level classification for EEG using Hidden Markov Models

EEG signal is one of the most important signals for diagnosing some diseases. EEG is always recorded with an amount of noise, the more noise is recorded the less quality is the EEG signal. The included noise can represent the quality of the recorded EEG signal, this paper proposes a signal quality assessment method for EEG signal. The method generates an automated measure to detect the noise level of the recorded EEG signal. Mel-Frequency Cepstrum Coefficient is used to represent the signals. Hidden Markov Models were used to build a classification model that classifies the EEG signals based on the noise level associated with the signal. This EEG quality assessment measure will help doctors and researchers to focus on the patterns in the signal that have high signal to noise ratio and carry more information. Moreover, our model was applied on an uncontrolled environment and on controlled environment and a result comparison was applied.

Prosthetic motor imaginary task classification using single channel of electroencephalography

Brain Computer Interface (BCI) is playing a very important role in human machine communications. Recent communication systems depend on the brain signals for communication. In these systems, users clearly manipulate their brain activity rather than using motor movements in order to generate signals that could be used to give commands and control any communication devices, robots or computers. In this paper, the aim was to estimate the performance of a brain computer interface (BCI) system by detecting the prosthetic motor imaginary tasks by using only a single channel of electroencephalography (EEG). The participant is asked to imagine moving his arm up or down and our system detects the movement based on the participant brain signal. Some features are extracted from the brain signal using Mel-Frequency Cepstrum Coefficient and based on these feature a Hidden Markov model is used to help in knowing if the participant imagined moving up or down. The major advantage in our method is that only one channel is needed to take the decision. Moreover, the method is online which means that it can give the decision as soon as the signal is given to the system. Hundred signals were used for testing, on average 89 % of the up down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial prosthetic limbs and wheelchairs due to it's high speed and accuracy.

Acoustic characterisation of cinema screens

As a consequence of cinema screens being placed in front of screen-speakers, a reduction in sound quality has been noticed. Cinema screens not only let the sound go through them, but also absorb a small amount of it and reflect the sound which impacts on the screen to the back, coming forward again in case it impacts on the loudspeaker. This backwards reflection in addition to the signal coming from the loudspeaker can lead to constructive or destructive interference at certain frequencies which usually results in comb filtering. In this project, this effect has been studied through researching amongst various data sheet provided by different manufacturers, acoustical measurements completed in the large anechoic chamber of the ISVR and some theoretical models developed with MatLab software. If results obtained with MatLab are accurate enough in comparison to the real measurements taken in the anechoic chamber this would lead to a good way to predict which would be the attenuation added to the system at each frequency, given that not all manufacturers provide an attenuation curve, but only an average attenuation. This average attenuation might be useless as sound waves have different wavelengths and its propagation through partitions varies. In fact, sound is composed by high and low frequencies, where high frequencies are characterised by a small wavelength which is usually easier to attenuate than low frequencies that characterised by bigger wavelengths. Furthermore, this information would be of great value to both screen manufacturers, who could offer a much more precise data in their data sheets; and customers, who would have a great amount of information to their disposal before purchasing and installing anything in their cinemas, being able to know by themselves which screen or loudspeaker should be best to meet their expectative. RESUMEN. La aparición de la digitalización de las bandas sonoras para las películas hace posible la mejora en la calidad de sonido de los cines. Sin embargo, un aspecto a tener en cuenta en esta calidad del sonido es la transmisión de éste a través de la pantalla, ya que normalmente tras ella se encuentran situados los altavoces. Las propiedades acústicas varían dependiendo del tipo de pantalla que se utilice, además de haber poca información a la que acceder para poder valorar su comportamiento. A lo largo de este proyecto, se analizan tres muestras de pantallas distintas donadas por distintos fabricantes para poder llegar a la conclusión de dependiendo del tipo de pantalla cuál es la distancia óptima a la que localizar la pantalla respecto al altavoz y con qué inclinación. Dicho análisis se realizó en la cámara anecoica del ISVR (University of Southampton) mediante la construcción de un marco de madera de 2x2 m en el que tensar las pantallas de cine, y un altavoz cuyo comportamiento sea el más similar al de los altavoces de pantalla reales. Los datos se captaron mediante cuatro micrófonos colocados en posiciones distintas y conectados al software Pulse de Brüel & Kjær, a través del cual se obtuvieron las respuestas en frecuencia del altavoz sin pantalla y con ella a diferentes distancias del altavoz. Posteriormente, los datos se analizaron con MatLab donde se calculó la atenuación, el factor de transmisión de la presión (PTF) y el análisis cepstrum. Finalmente, se realizó un modelo teórico del comportamiento de las pantallas perforadas basado en las placas perforadas utilizadas para atenuar el sonido entre distintas habitaciones. Como conclusión se llegó a que las pantallas curvadas son acústicamente más transparentes que las pantallas perforadas que a partir de 6 kHz son más acústicamente opacas. En las pantallas perforadas la atenuación depende del número de perforaciones por unidad de área y el diámetro de éstas. Dicha atenuación se reducirá si se reduce el diámetro de las perforaciones de la pantalla, o si se incrementa la cantidad de perforaciones. Acerca del efecto filtro peine, para obtener la mínima amplitud de éste la pantalla se deberá situar a una distancia entre 15 y 30 cm del altavoz, encontrando a la distancia de 30 cm que la última reflexión analizada a través de Cepstrum llega 5 ms más tarde que la señal directa, por lo cual no debería dañar el sonido ni la claridad del habla.

Aplicação de técnicas de classificação em séries temporais

Hoje, há um interesse crescente pela aprendizagem de uma segunda língua, quer seja por razões profissionais ou pessoais. Esta é uma tendência que se vai afirmando num mundo cada vez mais interconectado. Por outro lado, a democratização das tecnologias computacionais torna possível pensar em desenvolver novas técnicas de ensino de línguas mais automatizadas e personalizadas. Esta dissertação teve como objetivo estudar e implementar um conjunto de técnicas de processamento de sinal e de classificação de séries temporais úteis para o desenvolvimento de metodologias do ensino oral com feedback automático. São apresentados resultados preliminares sobre a prestação destas técnicas, e avaliada a viabilidade deste tipo de abordagem.

Electroencephalogram cepstral distances in alzheimer’s disease diagnosis

Alzheimer's disease (AD) represents one ofthe greatest public health challenges worldwide nowadays, because it affects millions of people ali o ver the world and it is expected that the disease will increase considerably in the near future. This study is the first application attempt of cepstral analysis on Electroencephalogram (EEG) signals to find new parameters in arder to achieve a better differentiation belween EEGs of AD patients and Control subjects. The results show that the methodology that uses a combined Wavelet (WT) Biorthogonal (Bior) 3.5 and cepstrum analysis was able to describe the EEG dynamics with a higher discriminative power than the other WTs/spectmm methodologies m previous studies. The most important significance figures were found in cepstral distances between cepstrums oftheta and alpha bands (p=0. 00006<0. 05).

Unified selective sorting approach to analyse multi-electrode extracellular data

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.