Neural Engineering / Neuroengineering / Brain Engineering
Mohammad Reza Nazari; Mohammad Reza Daliri; Ali Motie Nasrabadi
Volume 16, Issue 1 , May 2022, , Pages 51-62
Abstract
Visual attention as a cognitive factor plays a significant role in the processing of higher-order mental information that happens in the brain and affects brain activity in various areas of the visual cortex. Among the various recording systems, local field potentials, due to their stability, robustness, ...
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Visual attention as a cognitive factor plays a significant role in the processing of higher-order mental information that happens in the brain and affects brain activity in various areas of the visual cortex. Among the various recording systems, local field potentials, due to their stability, robustness, and frequency content have received interest in brain structure and cognitive processing research, as well as brain-computer interface (BCI) systems. Hence, the extraction and interpretation of information from local field potential (LFP) signals during visual attention has been considered to control cognitive systems. Cross-frequency coupling (CFC) as one of the information encoding strategies in the brain plays a functional role in perception, working memory, and visual attention tasks. However, the role of CFC as informative features for spatial attention decoding has not been adequately investigated. This paper aims to examine spatial attention decoding using LFP signals recorded from the monkey middle temporal area (MT). For this purpose, phase-phase and phase-amplitude coupling features and machine learning algorithms have been employed. The results show that the highest decoding performance was achieved by applying selected optimal features and the support vector machine classifier (90.36%). Moreover, among the selected features, gamma-delta, gamma-alpha, and beta-delta coupling contain the most cognitive information and the most effective features to improve the decoding performance of spatial attention in the visual system. Generally, the results suggest that cross-frequency coupling of LFP signals contains significant information in spatial attention tasks, and can be used as a suitable alternative to the time-frequency features of brain signals in cognitive BCI systems.
Bioelectrics
Elham Dehghanpur Deharab; Peyvand Ghaderyan
Volume 15, Issue 4 , March 2022, , Pages 279-287
Abstract
Parkinson's disease (PD) is one of the most common types of dementia associated with motor impairments and affected performance of motor skills such as writing. Brain imaging techniques are the common methods used to diagnose PD, which are expensive or invasive, and their accuracy depends on the experience ...
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Parkinson's disease (PD) is one of the most common types of dementia associated with motor impairments and affected performance of motor skills such as writing. Brain imaging techniques are the common methods used to diagnose PD, which are expensive or invasive, and their accuracy depends on the experience and the skill of the physician. Therefore, the development of an automated, low cost, and reliable diagnostic system is desirable for researchers. In this study, a handwriting signal including cognitive and motor-perceptual components has been used as a non-invasive, cost effective and reliable characteristic in identifying PD-related cognitive and motor dysfunctions. For this purpose, the matching pursuit algorithm with high time-frequency resolution has been employed to decompose X-Y coordinates. It provides a sparse representation of the handwriting signals and quantifies the basic information about the local changes in the handwriting signals. The proposed method is evaluated on a database with 31 healthy samples and 29 Parkinson's samples using the support vector machine classifier and obtained results yields an average accuracy rate of 90%, sensitivity rate of 91.59% and specificity rate of 90%. Comparing different writing tasks has also demonstrated superior performance of writing an entire sentence for PD detection.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Saeideh Davoodi; Mohammad Reza Daliri
Volume 11, Issue 3 , September 2017, , Pages 265-273
Abstract
Variety of brain region function represent that interactions between different frequency bands, employ general mechanisms of neural communications. Moreover, a method which recently used for information encoding in the brain is phase synchronization that is a process by which two or more cyclic signals ...
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Variety of brain region function represent that interactions between different frequency bands, employ general mechanisms of neural communications. Moreover, a method which recently used for information encoding in the brain is phase synchronization that is a process by which two or more cyclic signals tends to oscillate with a repeating sequence of relative phase angle. Some evidence demonstrated the important role of phase synchronization in cognitive tasks. In this paper we investigated the role of phase synchronization in a new visual discrimination task. For this purpose we collected electroencephalography signals from fifteen subjects during a color discrimination task. The machine learning algorithm, support vector machine (SVM), was used to find out whether this criterion can distinguish two different colors in the mentioned task. The results show that classification accuracy of 75% is achieved using phase synchronization feature. Also efficient frequency bands and contribution of effective electrodes were shown.
Speech processing
Hamid Azadi; Mohammad Ali Khalil Zade; Mohammad Reza Akbarzade Toutounchi; Hamid Reza Kobravi; Fariborz Rezaei Talab; Seyed Amir Ziafati Bagherzade; Alireza Noei Sarcheshme; Nina Shahsavan Pour
Volume 10, Issue 1 , May 2016, , Pages 41-47
Abstract
In recent years, researchers have tried hardly to diagnose Parkinson's disease through finding its relation with the patient's speech signal. Also, many studies have been performed on determining the intensity of the disease and its relation with vocal impairment measures. In this paper, we aim to assess ...
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In recent years, researchers have tried hardly to diagnose Parkinson's disease through finding its relation with the patient's speech signal. Also, many studies have been performed on determining the intensity of the disease and its relation with vocal impairment measures. In this paper, we aim to assess and compare the ability of extracting different feature sets from speech signal in order to Parkinson's disease diagnosis. Therefore, 132 features were used to measure vocal impairments from the voice signal of individuals vocalizing phoneme /a/. Then, we used RELIEF feature selection method and applied it to Support Vector Machine (SVM) classifier to choose the best feature of each class. A comparison was made between different feature sets, and finally discrimination percent 95.93 was reached to separate patients from the healthy ones using the combination of selected features. Results obtained from this research can be a very important step toward diagnosing Parkinson's disease non-invasively.
Saeed Rashidi; Ali Fallah; Farzad Towhidkhah
Volume 4, Issue 3 , June 2010, , Pages 219-230
Abstract
Nowadays, fast and accurate algorithms for signature verification are very attractive. In the area of dynamic signature verification, the features are classified into two groups: parametric and functional features. In parametric algorithms, although the speed of features extraction and classification ...
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Nowadays, fast and accurate algorithms for signature verification are very attractive. In the area of dynamic signature verification, the features are classified into two groups: parametric and functional features. In parametric algorithms, although the speed of features extraction and classification process is faster than function based approaches but they are less accurate. The goal of this paper is modeling of the velocity signal that its pattern and properties are stable for a person. With using pole-zero models based on discrete cosine transform, a precise method is proposed for modeling and then features are extracted from strokes. These features are the deference of pole angles of strokes. Applying linear, parzen window and support vector machine classifiers, the proposed algorithm was tested on data set from Persian, Chinese, English and Turkish people and with common threshold, resulted equal error rates of 1.25% and 1.78% in the random and skilled forgeries, respectively.
