Schimbati pe limba Romana - Romanian Main Frame (acces to all the site)

Gheorghe Asachi Technical University
Faculty of Electronics, Telecommunications and Information Technology

Course of Peripheral Equipment and Human Computer Interface
Teacher::  PhD, Associate Professor Dobrea Dan Marius

Year of study:

Home page:
Laboratory facilities:

Computer engineering
Department of Applied Electronics and Intelligent Systems
Faculty of Electronics and Telecommunications
Tehnical University "Gh. Asachi"

room III-26

Course Description:

    Human-Computer Interface is, basically, a discipline concerned with the design, evaluation, and implementation of interactive computing systems for human use; its aim is to build "intelligent" programs or systems that are sensitive to the need of the user through the physical and psychological state identification.
    This course and its labs present techniques for external data acquisition and introduce techniques for pattern recognition in order to classify the extracted physiological signals features.
    Simultaneously with the course interactive demonstrations, in NeuroSolution and Matlab, are presented. These interactive demonstrations intend to stimulate interest and help students to gain intuition about how classifier systems work under a variety of situations and constrains.
    Upon completion of this curse, students should be able to: acquire biomedical signals, have fundamental background in classifier system, be able to implement a classifier system in C++/C language and understand the role and drawbacks of each stage and method used in pattern recognition systems.

Class Meets:


Each Wednesday, starting at 14:00 and goes up to 17:00 (with 10 minutes break at 14.50 and 15.50) in Room P2, from February 17 to 16 May



Final Exam: 60% (The final exam will be closed book and will cover material from lectures and homework assignments.)
Project: 20%
Homework: 10%
Class Participation: 10%


The course cover the following topics:

1. Introduction

2. Cognitive limitation and particularity of human acquisition process

3. Phase stages in pattern recognition

3.1. Data acquisition
3.2. Signal preprocessing/processing
3.3. Features generation
3.4. Clustering
3.5. Classification

3.5.1. Introduction
3.5.2. Decision surface
3.5.3. Discriminate functions
3.5.3. Metrics

4. Elementary classifiers

4.1. Template matching
4.2. Minimum-distance classifiers
4.3. Limitations of simple classifiers

4.3.1. The features may be inadequate
4.3.2. The features may be correlated
4.3.3. The decision boundaries are not anymore linear
4.3.4. There are distinct subclasses in the data
4.3.5. The feature space may simply be too complex

5. Statistical Classifiers

5.1. Statistical Overview

5.1.1. Random vectors and their characterisation
5.1.2. Expectation and moments
5.1.3. The Gaussian density function
5.1.4. Linear transformation of random vectors
5.1.5. Diagonalization by unitary transformation
5.1.6. Diagonalization by triangular decomposition

5.2. Mahalanobis Classifiers

5.2.1. Mean and Variance
5.2.2. Mahalanobis Metric.
Mahalanobis Classifiers.

5.3. Bayesian Classifiers

5.3.1. Optimal Decision Boundary Based on Statistical Models of Data
5.3.2. A Two-Dimensional Pattern-Recognition Example
5.3.3. Discriminant Sensitivity to the Size of the Data
5.3.4. Features Selecton Baze on PDF

5.4 Nonparametric Method Used for Estimation and Classification

5.4.1. Density Estimation
5.4.2. Parzen Windows. Parzen Estimation.
5.4.3. K Nearest Neighbor Density Estimation
5.4.4. Nearest Neighbour Classification
5.4.5. The Nearest Neighbour Rule for Two Classes and N Classes
5.4.6. Error Rate fot the Nearest Neighbour Rule

6. Artificial Neuronal Network

6.1. Introduction

6.2. The Perceptron

6.2.1. Pattern Recognition Ability of the McCulloch-Pitts PE
6.2.2. The Perceptron

6.3. One Hidden Layer Multilayer Perceptrons

6.3.1. Discriminant Functions
6.3.2. Training the One Hidden Layer MLP
6.3.3. The Effect of the Number of Hidden Neurons

6.4. MLPs with Two Hidden Layers

6.4.1. Discriminant Functions
6.4.2. MLPs as Universal Classifier

6.5. Designing and Training MLPs

6.5.1. Learning Process Control
6.5.2. Methods to Improve of the Learning Process
6.5.3. Stop Criteria
6.5.5. Error Criterion
6.5.6. Network Size and Generalization


Lab syllabus:

    1. The graphical user interface of the LabWindowsCVI (2 hours)
    2. How to use timers in LabWindowsCVI ? (2 hours)
    3. The Graphic Controls of the LabWindowsCVI developement environment. (2 hours)
    4. The advance analysis library of the LabWindowsCVI (2 hours)
    5. The parallel port (LPT) (2 hours)
    6. The serial port 1 - Analog to digital conversion (2 hours)
    7. The serial port 2 - LCD communication (micrcontroler) (2 hours)
    8. National Instrument - Digital Acquisition Board (2 hours)
    9. A noncontact system used to respiration acquisition (2 hours)
    10. Visual pattern recognition (2 hours)
    11. K-means clustering and the silhouette parameter (2 hours)
    12. Introduction into the NeurSolution environment for pattern classification (2 hours)
    13. Brain Computer Interface 1 - EEG preprocessing and features extraction (2 hours)
    14. Brain Computer Interface 2 - EEG classification (2 hours)



  1. Charles W. Therrien, Discrete Random Signals and Statisitcal Signal Processing, Printice-Hall International Inc., New Jersey, United States of America, 1992, ISBN 0-13-217985-7
  2. José C. Principe, Neil R. Euliano, W. Curt Lefebvre, Neural and Adaptive Systems: Fundamentals Through Simulations, John Wiley & Sons Inc., United States of America, ISBN: 0-471-35167-9, 2000
  3. Liviu Goras, Semnale Circuite si Sisteme, Editura "Gh. Asachi", Iasi, Romania, 1994, ISBN 973-96222-8-3
  4. Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, John Wiley & Sons Inc., New York, United States of America, 2001, ISBN 0-471-05669-3
  5. Victor Neagoe, Octavian Stanasila, Teoria Recunoasterii Formelor, Editura Academiei Romane, Bucuresti, România, 1992, ISBN: 973-27-0341-5


Additional Literature:

    1. R. Picard, E. Vyzas, J. Healy, Toward Machine Emotional Intelligence: Analysis of Affective Physiological State, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, no. 10, pp. 1175 – 1191, 2001
    2. K. H. Kim, S. W. Bang, S. R. Kim, Emotion recognition system using short-term monitoring of physiological signals, Medical & Biological Engineering & Computing 2004, Vol. 42, pp. 419 – 427, 2004


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