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Clinical evaluation of watermarked medical images

Zain JM, Fauzi AM, Aziz AA

Conf Proc IEEE Eng Med Biol Soc, 2007 10 20;2006:5459-62.
PMID: 17946306

Digital watermarking medical images provides security to the images. The purpose of this study was to see whether digitally watermarked images changed clinical diagnoses when assessed by radiologists. We embedded 256 bits watermark to various medical images in the region of non-interest (RONI) and 480K bits in both region of interest (ROI) and RONI. Our results showed that watermarking medical images did not alter clinical diagnoses. In addition, there was no difference in image quality when visually assessed by the medical radiologists. We therefore concluded that digital watermarking medical images were safe in terms of preserving image quality for clinical purposes.

Matched MeSH terms: Pattern Recognition, Automated*
Quantitative assessment of breast density from digitized mammograms into Tabar's patterns

Jamal N, Ng KH, Looi LM, McLean D, Zulfiqar A, Tan SP, et al.

Phys Med Biol, 2006 Nov 21;51(22):5843-57.
PMID: 17068368

We describe a semi-automated technique for the quantitative assessment of breast density from digitized mammograms in comparison with patterns suggested by Tabar. It was developed using the MATLAB-based graphical user interface applications. It is based on an interactive thresholding method, after a short automated method that shows the fibroglandular tissue area, breast area and breast density each time new thresholds are placed on the image. The breast density is taken as a percentage of the fibroglandular tissue to the breast tissue areas. It was tested in four different ways, namely by examining: (i) correlation of the quantitative assessment results with subjective classification, (ii) classification performance using the quantitative assessment technique, (iii) interobserver agreement and (iv) intraobserver agreement. The results of the quantitative assessment correlated well (r2 = 0.92) with the subjective Tabar patterns classified by the radiologist (correctly classified 83% of digitized mammograms). The average kappa coefficient for the agreement between the readers was 0.63. This indicated moderate agreement between the three observers in classifying breast density using the quantitative assessment technique. The kappa coefficient of 0.75 for intraobserver agreement reflected good agreement between two sets of readings. The technique may be useful as a supplement to the radiologist's assessment in classifying mammograms into Tabar's pattern associated with breast cancer risk.

Matched MeSH terms: Pattern Recognition, Automated/methods
Body surface area measurement and soft clustering for PASI area assessment

Hani AF, Prakasa E, Nugroho H, Affandi AM, Hussein SH

Annu Int Conf IEEE Eng Med Biol Soc, 2012;2012:4398-401.
PMID: 23366902 DOI: 10.1109/EMBC.2012.6346941

Psoriasis is a common skin disorder with a prevalence of 0.6 - 4.8% around the world. The most common is plaques psoriasis and it appears as red scaling plaques. Psoriasis is incurable but treatable in a long term treatment. Although PASI (Psoriasis Area and Severity Index) scoring is recognised as gold standard for psoriasis assessment, this method is still influenced by inter and intra-rater variation. An imaging and analysis system called α-PASI is developed to perform PASI scoring objectively. Percentage of lesion area to the body surface area is one of PASI parameter. In this paper, enhanced imaging methods are developed to improve the determination of body surface area (BSA) and lesion area. BSA determination method has been validated on medical mannequin. BSA accuracies obtained at four body regions are 97.80% (lower limb), 92.41% (trunk), 87.72% (upper limb), and 83.82% (head). By applying fuzzy c-means clustering algorithm, the membership functions of lesions area for PASI area scoring have been determined. Performance of scoring result has been tested with double assessment by α-PASI area algorithm on body region images from 46 patients. Kappa coefficients for α-PASI system are greater than or equal to 0.72 for all body regions (Head - 0.76, Upper limb - 0.81, Trunk - 0.85, Lower limb - 0.72). The overall kappa coefficient for the α-PASI area is 0.80 that can be categorised as substantial agreement. This shows that the α-PASI area system has a high reliability and can be used in psoriasis area assessment.

Matched MeSH terms: Pattern Recognition, Automated/methods*
Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design

Huan NJ, Palaniappan R

J Neural Eng, 2004 Sep;1(3):142-50.
PMID: 15876633

In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.

Matched MeSH terms: Pattern Recognition, Automated/methods*
A hybrid neural network system for pattern classification tasks with missing features

Lim CP, Leong JH, Kuan MM

IEEE Trans Pattern Anal Mach Intell, 2005 Apr;27(4):648-53.
PMID: 15794170

A hybrid neural network comprising Fuzzy ARTMAP and Fuzzy C-Means Clustering is proposed for pattern classification with incomplete training and test data. Two benchmark problems and a real medical pattern classification task are employed to evaluate the effectiveness of the hybrid network. The results are analyzed and compared with those from other methods.

Matched MeSH terms: Pattern Recognition, Automated/methods*
Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals

Sudarshan VK, Acharya UR, Oh SL, Adam M, Tan JH, Chua CK, et al.

Comput Biol Med, 2017 04 01;83:48-58.
PMID: 28231511 DOI: 10.1016/j.compbiomed.2017.01.019

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.

Matched MeSH terms: Pattern Recognition, Automated/methods
Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals

Adam M, Oh SL, Sudarshan VK, Koh JE, Hagiwara Y, Tan JH, et al.

Comput Methods Programs Biomed, 2018 Jul;161:133-143.
PMID: 29852956 DOI: 10.1016/j.cmpb.2018.04.018

Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.

Matched MeSH terms: Pattern Recognition, Automated*
Fulltext Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG)

Al-Faris AQ, Ngah UK, Isa NA, Shuaib IL

J Digit Imaging, 2014 Feb;27(1):133-44.
PMID: 24100762 DOI: 10.1007/s10278-013-9640-5

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures' results are less than 0.05, such as: relative overlap (p = 0.0002), misclassification rate (p = 0.045), true negative fraction (p = 0.0001) and sum of true volume fraction (p = 0.0001).

Matched MeSH terms: Pattern Recognition, Automated/methods*
Fulltext Primary stability recognition of the newly designed cementless femoral stem using digital signal processing

Baharuddin MY, Salleh ShH, Hamedi M, Zulkifly AH, Lee MH, Mohd Noor A, et al.

Biomed Res Int, 2014;2014:478248.
PMID: 24800230 DOI: 10.1155/2014/478248

Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing.

Matched MeSH terms: Pattern Recognition, Automated/methods*
Quantification of diffusion tensor imaging in normal white matter maturation of early childhood using an automated processing pipeline

Loh KB, Ramli N, Tan LK, Roziah M, Rahmat K, Ariffin H

Eur Radiol, 2012 Jul;22(7):1413-26.
PMID: 22434420 DOI: 10.1007/s00330-012-2396-3

OBJECTIVES: The degree and status of white matter myelination can be sensitively monitored using diffusion tensor imaging (DTI). This study looks at the measurement of fractional anistropy (FA) and mean diffusivity (MD) using an automated ROI with an existing DTI atlas.
METHODS: Anatomical MRI and structural DTI were performed cross-sectionally on 26 normal children (newborn to 48 months old), using 1.5-T MRI. The automated processing pipeline was implemented to convert diffusion-weighted images into the NIfTI format. DTI-TK software was used to register the processed images to the ICBM DTI-81 atlas, while AFNI software was used for automated atlas-based volumes of interest (VOIs) and statistical value extraction.
RESULTS: DTI exhibited consistent grey-white matter contrast. Triphasic temporal variation of the FA and MD values was noted, with FA increasing and MD decreasing rapidly early in the first 12 months. The second phase lasted 12-24 months during which the rate of FA and MD changes was reduced. After 24 months, the FA and MD values plateaued.
CONCLUSION: DTI is a superior technique to conventional MR imaging in depicting WM maturation. The use of the automated processing pipeline provides a reliable environment for quantitative analysis of high-throughput DTI data.
KEY POINTS: Diffusion tensor imaging outperforms conventional MRI in depicting white matter maturation. • DTI will become an important clinical tool for diagnosing paediatric neurological diseases. • DTI appears especially helpful for developmental abnormalities, tumours and white matter disease. • An automated processing pipeline assists quantitative analysis of high throughput DTI data.

Matched MeSH terms: Pattern Recognition, Automated/methods
Brain white and gray matter anatomy of MRI segmentation based on tissue evaluation

Salih QA, Ramli AR, Mahmud R, Wirza R

MedGenMed, 2005;7(2):1.
PMID: 16369380

Different approaches to gray and white matter measurements in magnetic resonance imaging (MRI) have been studied. For clinical use, the estimated values must be reliable and accurate when, unfortunately, many techniques fail on these criteria in an unrestricted clinical environment. A recent method for tissue clusterization in MRI analysis has the advantage of great simplicity, and it takes the account of partial volume effects. In this study, we will evaluate the intensity of MR sequences known as T1-weighted images in an axial sliced section. Intensity group clustering algorithms are proposed to achieve further diagnosis for brain MRI, which has been hardly studied. Subjective study has been suggested to evaluate the clustering group intensity in order to obtain the best diagnosis as well as better detection for the suspected cases. This technique makes use of image tissue biases of intensity value pixels to provide 2 regions of interest as techniques. Moreover, the original mathematic solution could still be used with a specific set of modern sequences. There are many advantages to generalize the solution, which give far more scope for application and greater accuracy.

Matched MeSH terms: Pattern Recognition, Automated/methods*
Semi-quantitative and qualitative assessment of breast ultrasound elastography in differentiating between malignant and benign lesions

Alhabshi SM, Rahmat K, Abdul Halim N, Aziz S, Radhika S, Gan GC, et al.

Ultrasound Med Biol, 2013 Apr;39(4):568-78.
PMID: 23384468 DOI: 10.1016/j.ultrasmedbio.2012.10.016

The purpose of this study was to evaluate the diagnostic value of qualitative and semi-quantitative assessment of ultrasound elastography in differentiating between benign and malignant breast lesions. This prospective study was conducted in two tertiary medical centers. Consecutive B-mode ultrasound and real-time elastographic images were obtained for 67 malignant and 101 benign breast lesions in 168 women. Four experienced radiologists analyzed B-mode ultrasound alone and B-mode ultrasound combined with elastography independently. Conventional ultrasound findings were classified according to the American College of Radiology Breast Imaging Reporting and Data System classification. The elastographic assessment was based on qualitative and semi-quantitative parameters (i.e., strain pattern, width ratio, strain ratio). The sensitivity and specificity of combined elastography and conventional ultrasound were significantly higher than that of conventional ultrasound alone. The sensitivity, specificity, positive predictive value and negative predictive value was 97%, 61.4%, 62.5% and 96.8%, respectively, for conventional ultrasound and 100%, 93%, 99% and 90%, respectively, for combined technique. The semi-quantitative assessment with strain ratio and width ratio in elastography were the most useful parameters in differentiating between benign and malignant breast lesions. Cut-off point values for width ratio of more than 1.1 and strain ratio of more than 5.6 showed a high predictive value of malignancy with specificities of 84% and 76%, respectively (p

Matched MeSH terms: Pattern Recognition, Automated/methods*
Fingerprint verification prediction model in hand dermatitis

Lee CK, Chang CC, Johor A, Othman P, Baba R

Int J Dermatol, 2015 Jul;54(7):765-70.
PMID: 25427962 DOI: 10.1111/ijd.12451

Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis.

Matched MeSH terms: Pattern Recognition, Automated
Fulltext Detection of neovascularization in diabetic retinopathy

Hassan SS, Bong DB, Premsenthil M

J Digit Imaging, 2012 Jun;25(3):437-44.
PMID: 21901535 DOI: 10.1007/s10278-011-9418-6

Diabetic retinopathy has become an increasingly important cause of blindness. Nevertheless, vision loss can be prevented from early detection of diabetic retinopathy and monitor with regular examination. Common automatic detection of retinal abnormalities is for microaneurysms, hemorrhages, hard exudates, and cotton wool spot. However, there is a worse case of retinal abnormality, but not much research was done to detect it. It is neovascularization where new blood vessels grow due to extensive lack of oxygen in the retinal capillaries. This paper shows that various combination of techniques such as image normalization, compactness classifier, morphology-based operator, Gaussian filtering, and thresholding techniques were used in developing of neovascularization detection. A function matrix box was added in order to classify the neovascularization from natural blood vessel. A region-based neovascularization classification was attempted as a diagnostic accuracy. The developed method was tested on images from different database sources with varying quality and image resolution. It shows that specificity and sensitivity results were 89.4% and 63.9%, respectively. The proposed approach yield encouraging results for future development.

Matched MeSH terms: Pattern Recognition, Automated
A decision support system for automatic screening of non-proliferative diabetic retinopathy

Reza AW, Eswaran C

J Med Syst, 2011 Feb;35(1):17-24.
PMID: 20703589 DOI: 10.1007/s10916-009-9337-y

The increasing number of diabetic retinopathy (DR) cases world wide demands the development of an automated decision support system for quick and cost-effective screening of DR. We present an automatic screening system for detecting the early stage of DR, which is known as non-proliferative diabetic retinopathy (NPDR). The proposed system involves processing of fundus images for extraction of abnormal signs, such as hard exudates, cotton wool spots, and large plaque of hard exudates. A rule based classifier is used for classifying the DR into two classes, namely, normal and abnormal. The abnormal NPDR is further classified into three levels, namely, mild, moderate, and severe. To evaluate the performance of the proposed decision support framework, the algorithms have been tested on the images of STARE database. The results obtained from this study show that the proposed system can detect the bright lesions with an average accuracy of about 97%. The study further shows promising results in classifying the bright lesions correctly according to NPDR severity levels.

Matched MeSH terms: Pattern Recognition, Automated
Developmental Approach for Behavior Learning Using Primitive Motion Skills

Dawood F, Loo CK

Int J Neural Syst, 2018 May;28(4):1750038.
PMID: 29022403 DOI: 10.1142/S0129065717500381

Imitation learning through self-exploration is essential in developing sensorimotor skills. Most developmental theories emphasize that social interactions, especially understanding of observed actions, could be first achieved through imitation, yet the discussion on the origin of primitive imitative abilities is often neglected, referring instead to the possibility of its innateness. This paper presents a developmental model of imitation learning based on the hypothesis that humanoid robot acquires imitative abilities as induced by sensorimotor associative learning through self-exploration. In designing such learning system, several key issues will be addressed: automatic segmentation of the observed actions into motion primitives using raw images acquired from the camera without requiring any kinematic model; incremental learning of spatio-temporal motion sequences to dynamically generates a topological structure in a self-stabilizing manner; organization of the learned data for easy and efficient retrieval using a dynamic associative memory; and utilizing segmented motion primitives to generate complex behavior by the combining these motion primitives. In our experiment, the self-posture is acquired through observing the image of its own body posture while performing the action in front of a mirror through body babbling. The complete architecture was evaluated by simulation and real robot experiments performed on DARwIn-OP humanoid robot.

Matched MeSH terms: Pattern Recognition, Automated
Glove-based approach to online signature verification

Kamel NS, Sayeed S, Ellis GA

IEEE Trans Pattern Anal Mach Intell, 2008 Jun;30(6):1109-13.
PMID: 18421114 DOI: 10.1109/TPAMI.2008.32

Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel on-line signature verification system using the Singular Value Decomposition (SVD) numerical tool for signature classification and verification is presented. The proposed technique is based on the Singular Value Decomposition in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, so the effective dimensionality of A can be reduced. Having modeled the data glove signature through its r-principal subspace, signature authentication is performed by finding the angles between the different subspaces. A demonstration of the data glove is presented as an effective high-bandwidth data entry device for signature verification. This SVD-based signature verification technique is tested and its performance is shown to be able to recognize forgery signatures with a false acceptance rate of less than 1.2%.

Matched MeSH terms: Pattern Recognition, Automated/methods*
Vocal fold vibratory characteristics of healthy geriatric females--analysis of high-speed digital images

Ahmad K, Yan Y, Bless D

J Voice, 2012 Nov;26(6):751-9.
PMID: 22633334 DOI: 10.1016/j.jvoice.2011.12.002

A high proportion of the geriatric population suffers from presbylaryngis and presbyphonia; however, our knowledge of vibratory patterns in this population is almost nonexistent. In this study, we investigate the vocal fold vibratory patterns of healthy elderly females to determine which features or combination of them could best describe the geriatric voices.

Matched MeSH terms: Pattern Recognition, Automated
Fulltext Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue

Alomari YM, Sheikh Abdullah SN, MdZin RR, Omar K

Comput Math Methods Med, 2015;2015:673658.
PMID: 25793010 DOI: 10.1155/2015/673658

Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.

Matched MeSH terms: Pattern Recognition, Automated
A new brain-computer interface design using fuzzy ARTMAP

Palaniappan R, Paramesran R, Nishida S, Saiwaki N

IEEE Trans Neural Syst Rehabil Eng, 2002 Sep;10(3):140-8.
PMID: 12503778

This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients.

Matched MeSH terms: Pattern Recognition, Automated

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