Advances in EEG-Based Biometry

Ferreira, António E. N.; Almeida, Carlos Frederico Meschini; Georgieva, Pétia; Tomé, Ana Maria; Silva, Filipe

doi:10.1007/978-3-642-13775-4_29

Cited by 18 publications

(15 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A further study on the deSNN model will enable more efficient real time applications such as: EEG pattern recognition for BCI (Ferreira et al, 2010;Lalor et al, 2005); fMRI pattern recognition (Sona et al, 2007); neuro-rehabilitation robotics (Wang et al, 2012), neuroprosthetics (Isa et al, 2009); cognitive robots (Bellas et al, 2010); personalized modeling (Kasabov and Hu, 2010) for the prognosis of fatal events such as stroke (Barker-Collo et al, 2010) and degenerative progression of brain disease, such as AD Kasabov (ed), 2013).…”

Section: Discussion Conclusion and Further Directionsmentioning

confidence: 99%

Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition

et al. 2013

View full text Add to dashboard Cite

On-line learning and recognition of spatio-and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as SpikeTiming Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes (which information is explicitly present in input data streams and would be crucial to consider in some tasks) and of the information contained in the timing of the following spikes that is learned by the dynamic synapses as a whole spatio-temporal pattern. The paper is published in Neural Networks, Elsevier, e- AbstractOn-line learning and recognition of spatio-and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant S...

show abstract

Section: Discussion Conclusion and Further Directionsmentioning

confidence: 99%

Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Examples of problems involving SSTD are: brain cognitive state evaluation based on spatially distributed EEG electrodes [70,26,51,21,99,100] (fig.1a); fMRI data [102] (fig.1b); moving object recognition from video data [23,60,25] (fig.15); spoken word recognition based on spectro-temporal audio data [93,107]; evaluating risk of disease, e.g. heart attack [20]; evaluating response of a disease to treatment based on clinical and environmental variables, e.g.…”

Section: Spatio-and Spectro-temporal Data Modelling and Pattern Recogmentioning

confidence: 99%

“…-Moving object recognition ( fig. 15) [23,60]; -EEG data modelling and pattern recognition [70,1,51,21,26,99,35,36] directed to practical applications, such as: BCI [51], classification of epilepsy [35,36,109] - (fig.16); -Robot control through EEG signals [86] (fig.17) and robot navigation [80]; -Sign language gesture recognition (e.g. the Brazilian sign language - fig.18) [95]; -Risk of event evaluation, e.g.…”

Section: Snn Software and Hardware Implementations To Support Stprmentioning

confidence: 99%

Evolving Spiking Neural Networks and Neurogenetic Systems for Spatio- and Spectro-Temporal Data Modelling and Pattern Recognition

Kasabov

2012

Advances in Computational Intelligence

View full text Add to dashboard Cite

“…The identification/ authentication systems built so far differ basically in filtering and classification components (Palaniappan & Mandic, 2007;Marcel & Millán, 2007). However, our initial study (Ferreira et al, 2010) has shown that the discrimination process is slightly dependent on the specific filter and classifier. Critical issues related with building an efficient EEG based biometry system are briefly discussed below.…”

Section: Eeg-based Biometrymentioning

confidence: 99%

“…The former tries to find out the user's intention and generates output commands for controlling an appropriate output device (Bento et al, 2008). The later explores the possibility of using the brain electrical activity during visual stimuli for implementing an EEG biometric system (Ferreira et al, 2010). The remainder of the chapter is organised as follows: Section 2 presents an overview of the activity at the IEETA (Institute of Electronic Engineering and Telematics of Aveiro) research unit.…”

mentioning

confidence: 99%