EEG Classification with BSA Spike Encoding. Algorithm and Evolving Probabilistic Spiking Neural. Network. Nuttapod Nuntalid1, Kshitij Dhoble1, and Nikola ...
EEG Classification with BSA Spike Encoding Algorithm and Evolving Probabilistic Spiking Neural Network Nuttapod Nuntalid1, Kshitij Dhoble1, and Nikola Kasabov1,2 , Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1010, New Zealand {nnuntali, kdhoble, nkasabov}@aut.ac.nz 2 Institute for Neuroinformatics, University of Zurich and ETH Zurich 1
Abstract.This study investigates the feasibility of Bens Spike Algorithm (BSA) to encode continuous EEG spatio-temporal data into input spike streams for a classification in a spiking neural network classifier. A novel evolving probabilistic spiking neural network reservoir (epSNNr) architecture is used for the purpose of learning and classifying the EEG signals after the BSA transformation. Experiments are conducted with EEG data measuring a cognitive state of a single individual under 4 different stimuli. A comparison is drawn between using traditional machine learning algorithms and using BSA plus epSNNr, when different probabilistic models of neurons are utilised. The comparison demonstrates that: (1) The BSA is a suitable transformation for EEG data into spike trains; (2) The performance of the epSNNr improves when a probabilistic model of a neuron is used, compared to the use of a deterministic LIF model of a neuron; (3) The classification accuracy of the EEG data in an epSNNr depends on the type of the probabilistic neuronal model used. The results suggest that an epSNNr can be optimised in terms of neuronal models used and parameters that would better match the noise and the dynamics of EEG data. Potential applications of the proposed method for BCI and medical studies are briefly discussed. Keywords: Spatio-Temporal Patterns, Electroencephalograms (EEG), Stochastic neuron models, evolving probabilistic spiking neural networks.
1 Introduction EEG refers to the recording of electrical brain signal activity that is acquired along the head scalp as a result of neuronal activity in brain. In clinical aspect, EEG have been used in clinical recording of the brain electrical activity over a period of time and usually employs 19 electrodes / channels that are placed over various locations on the scalp. In neurology, the main diagnostic application of EEG is in the case of epilepsy,
as epileptic seizure creates clear spike activities that can be measured in standard EEG equipment [1], [2]. A clinical application of EEG is to show the type and location of the activity in the brain during a seizure. It is also commonly used to evaluate people who are having problems related to brain functionality such as coma, tumours, long-term memory loss, or stroke. In computer science, there have been many studies that focus on EEG applications for Brain Computer Interfaces (BCI). EEG data analysis has been explored for a better understanding of the information processing capability of the mammalian brain. EEG can also be potentially used in biometric systems. Given that the brain-wave pattern of every individual is unique, EEG can be used for developing person identification or authentication systems [3], [4]. Having in mind the importance of the accurate analysis and study of EEG signals, we are aiming in this paper to propose a method for EEG data transformation into spike trains and their accurate classification. The rest of the paper is structured as follows: Section 2 describes related researches and motivations. Section 3 presents the proposed methodology that is been used in this experimentation. Section 4 contains comparative experimental results along with discussions. Finally, Section 5 summarizes the conclusion and future directions of our study.
2 Related Works Spiking neuron networks (SNN) have been used for EEG analysis in some researches, and have shown remarkable performance in comparison to other traditional methods for classification task. In [5], the authors have proposed a method for the creation of spiking neural networks for EEG classification of epilepsy data for the purpose of epileptic seizure detection. Their experiment used a simple 3-layer feed-forward architecture (having input layer, hidden layer, output layer) which resulted in an average classification accuracy of approximately 90.7%. In a recent study [6], the researchers analysed rat’s EEG data using reservoir computing approach (echo state network) for epileptic seizure detection in real-time, based on a data from 4 EEG channels. It is a two class problem, where they had to classify the EEG signals for detection of seizure and tonic-seizure. The reservoir was made of 200 Leaky Integrate-and Fire (LIF) neurons, where 20% and 80% of the EEG data was used for training and testing respectively. The results of this study claimed that the performance was higher than the other four traditional methods in terms of detection time, which was around 85% accuracy in 0.5 seconds for seizure and 85% accuracy in 3 seconds for tonic-seizure. However, this study was done by using EEG data from a rat, acquired from only 4 channels and the frequency for detecting seizure/s was known in advance (8, 16 and 24 Hz). Hence, recent studies on SNN and reservoir computing for EEG application shows that many of them produced comparatively good results while utilizing a deterministic neuronal model. However, in one of our recent work [7], we have shown that replacing the deterministic with the probabilistic spiking neuron models yields better results.
In this study we aim to analyze the feasibility of BSA spike encoding scheme along with a SNN reservoir such as LSM using probabilistic spiking neuron models for complex spatio-temporal human EEG data, acquired from 64 channels. In the following we have described the design of our experiment and its setup.
3 Methodology The framework for classification of spatio-temporal data based on evolving probabilistic spiking neural network reservoir (epSNNr) paradigm is presented in Fig.1. At first, each channel of spatial-temporal data (EEG) is transformed into trains of spikes by the encoder module. Then the trains of spikes are distributed into spatiotemporal filter which employs the latest reservoir paradigm (i.e. LSM) that utilizes several stochastic neuron models as liquid generators [7]. Further, the filter generates liquid state for each time step. These states are fed into a readout function for training and testing the classification performance using a pre-defined type of a classifier.
Fig. 1. Framework for EEG spatio-temporal pattern learning and classification based on epSNNr.
3.1 Spike Encoder In our methodology we have incorporated BSA spike encoding scheme. So far, this encoding scheme has only been used for encoding sound data. However, since EEG signals also fall under the frequency domain, we hypothesised that BSA encoding will be suitable to transform EEG signals into spike representation. The key benefit of using BSA is that the frequency and amplitude features are smoother in comparison to the HSA (Hough Spiker Algorithm) spike encoding scheme [8]. Moreover, due to the smoother threshold optimization curve, it is also less susceptible to changes in the filter and the threshold [8]. Studies have shown that this method offers an improvement of 10dB-15dB over the HSA spike encoding scheme. According to [8], the stimulus is estimated from the spike train by
where, tk represents the neurons firing time, h(t) denotes the linear filters impulse response and x(t) is the spike of the neuron that can be calculated as
For this particular dataset, we have set the Finite Impulse Response (FIR) filter size to 20, and the BSA threshold to 0.955.
Fig. 2. The top figure shows the Actual one channel EEG signal for the duration of 20ms. The middle figure is the spike representation of the above figure obtained using BSA. The bottom figure shows the actual one channel EEG signal that has been superimposed with another signal (dashed lines) which represents the reconstructed EEG signal from the BSA encoded spikes. The similarity between the two signals is obvious that illustrates the applicability of the BSA transformation.
However, when the spike train x(t) is applied with a discrete FIR filter, the Eq.2 can be represented as
where, M refers to the number of filter taps. A more detailed explanation is given in [8]. 3.2 Evolving probabilistic spiking neuron network reservoir (epSNNr) In epSNNr, we have replaced the deterministic LIF neurons of a traditional LSM with probabilistic neural models that have been comprehensively described in [7]. The probabilistic approach has been inspired by biological neurons that exhibit substantial stochastic characteristics. Therefore, incorporation of non-deterministic elements into the neural model may provide us with advantage due to the brain-like information processing system. In our reservoir we have used the standard Leaky Integrate and Fire (LIF) neuron model as well as probabilistic models such as Noisy Reset (NR), Step-wise Noisy Threshold (ST) and Continuous Noisy Threshold (CT) (see fig.3. for an illustration of the difference between the three stochastic neuronal models). The advantage of stochastic neural models has been demonstrated in a previous study [7]. Table 1. The following table provides the parameter setting that has been used in our experimental setup for the epSNNr.
3.3 Dataset RIKEN EEG Dataset was collected in the RIKEN Brain Science Institute in Japan. It includes 4 stimulus conditions (4 classes): Class1 - Auditory stimulus; Class2 -Visual stimulus; Class3 - Mixed auditory and visual stimuli; Class4 -No stimulus. The EEG data were acquired using a 64 electrode EEG system that was filtered using a 0.05Hz to 500 Hz band- pass filter and sampled at 2KHz. According to the sample rate, the dataset is instable. In this preliminary proof of concept investigation we collected a small number of data points: 11 epochs from 50 epochs (1988-2153 samples/epoch/50ms) of each class (4 classes are 44 epochs) which have closer rate as possible. We used 80% (9 epochs) and 20% (2 epochs) for training and testing respectively.
Fig. 3. Evolution of the post-synaptic potential u(t) and the firing threshold over time (blue (dark) and yellow (light) curves respectively recorded from a single neuron of each of the three stochastic neural models used in this paper vs the standard LIF model. The input stimulus for each neuron is shown at the top of the diagram. The output spikes of each neuron are shown as thick vertical lines above the corresponding threshold curve (from [7])
4 Experiments and Discussions The experimental setup of this study is presented in Fig.1. All networks have the same network topology and the same connection weight matrix. A detailed description of the network generation and parameterisation is given in Table 1. We construct a reservoir having a small-world interconnectivity pattern as described in [9]. In order to make a standard comparison in our further investigation, the recurrent SNN is generated by using Brain[10] whose grid alignment is similar to the CSIM’s (A neural Circuit Simulation) default LSM setting having 135 neurons in a three-dimensional grid of size 9 × 5 × 3. In this grid, two neurons A and B are connected with a connection probability
The sample rate of the EEG dataset is extremely higher than usual EEG datasets, where each epoch belonging to a class (having 1988-2153 samples/epoch) was encoded into 50ms spike trains which are then transformed to 500ms, in order to normalise the parameters and simulation time steps. The liquid responses from the network, which are shown in fig.4, were mapped into 25ms time-bins (20 time−bins/epoch). This particular setting resulted in an optimal accuracy for this experimental setup. There are two readout functions in this investigation. The first is none-adaptive Naivebayes whose Numeric estimator precision values are chosen based on analysis of the training data. The Second is Multi-Layered Perceptron(MLP) which utilizes 139 sigmoid nodes for hidden layer (number of input attributes plus 4 stimuli), 0.3 for learning rate, 0.2 for momentum, 500 training iterations, and validate threshold was set to 20.
In conventional method, parameters of Naivebayes and MLP were setup in the same way as the proposed method but MLP included only 68 sigmoid nodes for hidden layer (64 input plus 4 stimuli). The state time-bins from 1st to 9th epoch were used for training set (equivalent to 80%) and 10th to 11th epoch were used as test set. From table 2, it can be seen that the traditional classifiers do not perform optimally on the raw EEG data. However, when the raw EEG is applied with BSA spike encoder and is passed through epSNNr with various stochastic models and classifiers such as Naivebayes and MLP, they perform especially well. For our experiment we had considered various other classifiers but they were found to be inappropriate due to their inability to handle complex spatiotemporal EEG data. The accuracy obtained from epSNNr that utilizes Naivebayes have the same result for all the neuronal models, however the root mean squared error (RMSE) values (see Fig.5.) shows significant difference particularly for the ST model with Naivebayes, which is found to be the lowest, signifying the highest performance and stability in comparison with deterministic and other probabilistic models for this experiment. Our main results prove that transforming EEG signals into spike trains using the BSA spike encoding scheme results is significantly higher classification accuracy. A second result is that using a stochastic neuronal model in the epSNNr (e.g. the ST model) may lead to an improved accuracy (see the classification results for the MLP classifier from Table 2 and the root mean square error results from Fig.5).
5 Conclusion and Future Works In this study, we have shown that BSA spike encoding scheme is suitable for encoding EEG data stream. Moreover, we have also addressed the question whether probabilistic neural models are principally suitable liquids in the context of LSM. We have experimentally shown that, the performance of the epSNNr improves when a probabilistic model of a neuron is used, compared to the use of a deterministic LIF model of a neuron, and the classification performance of the EEG data in an epSNNr depends on the type of the probabilistic neuronal model used. The results suggest that an epSNNr can be optimised in terms of neuronal models used and parameters that would better match the noise and the dynamics of EEG data. Moreover, previous researches have had never incorporated 64 EEG channels. Our results have indicated potential advantages of using epSNNr along with the viability of BSA encoding scheme for EEG data streams that are spatio-temporal in nature which may contribute to BCI and medical studies. However, noise reduction and/or feature extraction methods, and optimization algorithm could also be employed possibly for both local and global optimization in our future study, since various parameters in the framework need to be adjusted. However, further study on the behavior of the epSNNr architecture under different conditions is needed and more experiments are required to be carried out on EEG datasets.
Fig. 4. The reservoir using ST Model response of one epoch of auditory stimulus is shown, where x axis represents time in 500ms and y axis is neurons. Table 2. The following table provides Classification Accuracy (%), for various methods.
Several methods will be investigated for the improvement of the epSNNr: Using dynamic selection of the ’chunk’ of input data entered into the epSNNr; A new algorithm for an evolving (adaptive) learning in the epSNNr will be developed so that the reservoir learns to discriminate better states that represent different class data. Using more complex probabilistic spiking neuron models, such as [11], would require dynamic optimization of its probabilistic parameters. We intend to use a gene regulatory network (GRN) model to represent the dynamics of these parameters in relation to the dynamics of the spiking activity of the epSNNr as suggested in [12]. Each of the probability parameter, the decay parameter, the threshold and other parameters of the neurons, will be represented as a function of particular genes for a set of genes related to the epSNN model, all genes being linked together in a dynamic GRN model. Furthermore, various parameters such as the connection probability, size and shape of the network topology shall also be tested. In this respect the soft winnertake-all topology will be investigated [13]. For applications that require on line training we intend to use evolving SNN classifier [14], [15]. Finally, implementation
of the developed models on existing SNN hardware [16], [17] will be studied especially for on-line learning and object recognition applications such as intelligent mobile robots [18].
Fig. 5. This figure shows the Root Mean Squared Error (RMSE) for various stochastic neuron models when applied to Naivebayes and MLP classifiers.
Acknowledgment The EEG data used in the experiments were collected in the RIKEN Brain Science Institute, Tokyo by a team lead by Case van Leuven and Andrjei Chihotsky. We developed a software simulator using the software environment Brian [18]. The work on this paper has been supported by the Knowledge Engineering and Discovery Research Institute (KEDRI, www.kedri.info), Auckland University of Technology. N.K. has been supported by a one year Marie Curie International Incoming Fellowship within the 7th European Framework Programme under the project ‘EvoSpike’, hosted by the Institute for Neuroinformatics at the University of Zurich and ETH Zurich.
References 1. Tatum, W.: Handbook of EEG interpretation. Demos Medical Publishing (2007) 2. Niedermeyer, E., Da Silva, F.: Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins (2005) 3. Marcel, S., Mill´an, J.: Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE transactions on pattern analysis and machine intelligence 743–752 (2007) 4. Palaniappan, R., Mandic, D.: EEG based biometric framework for automatic identity verification. The Journal of VLSI Signal Processing 49(2) 243–250 (2007)
5. 6. 7.
8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
Ghosh-Dastidar, S., Adeli, H.: A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural networks 22(10) 1419–1431 (2009) Buteneers, P., Schrauwen, B., Verstraeten, D., Stroobandt, D.: Real-time epileptic seizure detection on intra-cranial rat data using reservoir computing. Advances in NeuroInformation Processing 56–63 (2009) Schliebs, S., Nuntalid, N., Kasabov, N.: Towards spatio-temporal pattern recognition using evolving spiking neural networks. Proceedings of the 17th international conference on Neural information processing: theory and algorithms-Volume Part I, Springer-Verlag 163–170 (2010) Schrauwen, B., Van Campenhout, J.: BSA, a fast and accurate spike train encoding scheme. Proceedings of the International Joint Conference on Neural Networks. Volume 4., IEEE 2825–2830 (2003) Maass, W., Natschl¨ager, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural computation14(11) 2531–2560 (2002) Goodman, D., Brette, R.: The Brian simulator. Frontiers in Neuroscience 3(2) 192 (2009) Kasabov, N.: To spike or not to spike: A probabilistic spiking neuron model. Neural Networks 23(1) 16–19 (2010) Kasabov,N. and R.Schliebs and H.Kojima : Probabilistic Computational Neurogenetic Framework: From Modelling Cognitive Systems to Alzheimer’s Disease. IEEE Transactions on Autonomous Mental Development (2011) Rutishauser, U., Douglas, R., Slotine, J.: Collective Stability of Networks of WinnerTake-All Circuits. Neural Computation 1–39 (2011) Kasabov, N.: Evolving connectionist systems: the knowledge engineering approach. Springer-Verlag New York Inc (2007) Schliebs, S., Kasabov, N., Defoin-Platel, M.: On the Probabilistic Optimization of Spiking Neural Networks. International Journal of Neural Systems 20(6) 481–500 (2010) Indiveri, G., Stefanini, F., Chicca, E.: Spike-based learning with a generalized integrate and fire silicon neuron. Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE 1951–1954 (2010) Indiveri, G., Chicca, E., Douglas, R.: Artificial cognitive systems: From vlsi networks of spiking neurons to neuromorphic cognition. Cognitive Computation 1(2) 119–127 (2009) Bellas, F., Duro, R., Faina, A., Souto, D.: Multilevel Darwinist Brain (MDB): Artificial Evolution in a Cognitive Architecture for Real Robots. IEEE Transactions on Autonomous Mental Development 2(4) 340–354 (2010)
