The document presents a study on a novel multi-scale two-directional two-dimensional principal component analysis (ms2d2pca) method aimed at improving biomedical signal classification, particularly using high-dimensional data from various sources like EMG and EEG. This approach addresses challenges related to high-dimensional recordings which traditional methods struggle with due to dimensionality and sample size issues. Experimental results show that ms2d2pca effectively classifies hand movements from 89-channel EMG signals in stroke survivors.

1. Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Title: Multi-scale two-directional two-dimensional
principal component analysis and its application
to high-dimensional biomedical signal
classification
Abstract— Goal: Time-frequency analysis incorporating the wavelet transform
followed by principal component analysis(WT-PCA) has been a powerful
approach for the analysis of biomedical signals such as electromyography
(EMG),electroencephalography (EEG), electrocardiography (ECG), and Doppler
ultrasound. Time-frequency coefficients at various scales were usually transformed
into a one-dimensional array using only a single or a few signal channels. The
steady improvement of biomedical recording techniques has increasingly permitted
the registration of a high number of channels. However, WT-PCA is not applicable
to high-dimensional recordings due to the curse of dimensionality and small
sample size problem. In this study, wepresent a multi-scale two-directional two-
dimensional principal component analysis (MS2D2PCA) method for the efficient
and effective extraction of essential feature information from high-dimensional
signals. Multi-scale matrices constructed in the first step incorporate the spatial
correlation and physiological characteristics of sub-band signals among channels.
In the second step, the two-directional two-dimensional principal component
analysis operates on the multi-scale matrices to reduce the dimension, rather than
vectors in conventional PCA. Results are presented from an experiment to classify
20 hand movementsusing 89-channel EMG signals recorded in stroke survivors,
which illustrates the efficiency and effectiveness of the proposed method for high-
dimensional biomedical signal analysis.
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2. Software & Hardware requirement:
1. Hardware requirement:
1. PC
2. RAM minimum 2GB
3. HDD minimum 100GB
2. Software requirement:
1. MATLAB 7.0
2. Signal processing toolbox
3. Image processing toolbox
4. Mathematical toolbox
PROJECT FLOW:
First Review:
Literature Survey
Paper Explanation
Design of Project
Project Enhancement explanation
Second Review:
Implementing 40% of Base Paper
Third Review
Implementing Remaining 60% of Base Paper with Future Enhancement
(Modification)
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For More Details please contact
Logic Mind Technologies
Vijayangar (Near Maruthi Medicals), Bangalore-40
Ph: 8123668124 // 8123668066
Mail: logicmindtech@gmail.com