Classification of driver's cognitive responses from EEG analysis
ABSTRACT During the past years, the growing number of traffic fatalities has become an important issue in public security. In this paper, we develop a quantitative analysis for ongoing assessment of cognitive response by investigating the neurobiological brain dynamics in traffic-light experiments. A single-trial event-related-potential (ERP)-based fuzzy neural network (FNN) is applied to recognize different brain potentials stimulated by red/green/yellow traffic-light events. The system consists of a dynamic virtual-reality (VR)-based motion simulation platform, EEG signal detection and analysis units, and FNN-based classifier. ICA algorithms are used to obtain noise-free ERP signals from the multi-channel EEG signals. A novel temporal filter is also proposed to solve time-alignment problems of ERP features and PCA is used to reduce feature dimensions, which were then fed into a FNN classifier. Experimental results demonstrate the feasibility of detecting and analyzing multiple streams of ERP signals that organize operators' cognitive responses to task events. Comparisons of three kinds of linear and nonlinear classifiers show that our proposed FNN-based classifier can achieve a satisfactory and superior recognition rate (85%). The classification results can be further transformed as the control/biofeedback signals of intelligent driving systems.
- SourceAvailable from: Andras Kemeny[show abstract] [hide abstract]
ABSTRACT: The use of driving simulation for vehicle design and driver perception studies is expanding rapidly. This is largely because simulation saves engineering time and costs, and can be used for studies of road and traffic safety. How applicable driving simulation is to the real world is unclear however, because analyses of perceptual criteria carried out in driving simulation experiments are controversial. On the one hand, recent data suggest that, in driving simulators with a large field of view, longitudinal speed can be estimated correctly from visual information. On the other hand, recent psychophysical studies have revealed an unexpectedly important contribution of vestibular cues in distance perception and steering, prompting a re-evaluation of the role of visuo-vestibular interaction in driving simulation studies.Trends in Cognitive Sciences 02/2003; 7(1):31-37. · 16.01 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes initially in the RSONFIN. They are created online via concurrent structure identification and parameter identification. The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these resultsIEEE Transactions on Neural Networks 08/1999; · 2.95 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes. Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automaticallyIEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 09/1997; · 3.24 Impact Factor