A neural minimum input model to reconstruct the electrical cortical activity

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L.M. Roa Romero (ed.), XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, IFMBE Proceedings 41,

639 DOI: 10.1007/978-3-319-00846-2_158, © Springer International Publishing Switzerland 2014

A Neural Minimum Input Model to Reconstruct the Electrical Cortical Activity

S. Conforto1,2, I. Bernabucci1, N. Accornero3, M. Bertollo2, C. Robazza2, S. Comani2,

M. Schmid1,2,and T. D’Alessio1 1

Department of Engineering, University Roma TRE, Rome, Italy 2

BIND - Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio”, Chieti-Pescara, Italy 3

Department of Neurology and Psychiatry, Sapienza University of Rome, Italy

Abstract—In recent years, technology has allowed the

pro-gressive increase in the number of channels for EEG record-ing. The scientific rationale is the demand for an increase of the spatial resolution of the recording to better locate the sources of the underlying cortical activity. Despite some papers confirm the improvement of the spatial resolution by using 256 channels we wonder if in fact this density of electrodes on the scalp does not constitute an useless spatial oversampling. Thus we set out to determine whether the amount information de-rived from a standard 19 channel EEG recording was obtaina-ble with a smaller number of electrodes, in particular with a mounting to 8 channels.

Were used and compared the performance of a Perceptron, a Feed-Forward and a Recurrent neural networks, after su-pervised training by the back-propagation algorithm. The target was to reconstruct the signals of all the 19 channels starting from only 8 input channels. The data-set was built by using multi-subjects 19 channels recordings containing exam-ples of normal, generalized and focal abnormal EEG activity. All the types of network have been able to reconstruct the missing channels with an error lower than 1%. From this pilot study seems to conclude that the information content of this 8-channel EEG is equivalent to that obtainable with a number of channels more than double. Further developments will check the optimal ratio between the number of recorded and recon-structed channels and the applicability of the approach in real-life contexts.

Keywords—Artificial Neural Network, EEG, Spectral

Anal-ysis, Time AnalAnal-ysis, Amplitude Maps.

I.

I

NTRODUCTION

Recent trends in neurophysiology aim at increasing the number of channels in EEG acquisition systems, looking for an improvement of the measurement's spatial resolution, deemed necessary to better localized the sources of cortical activity.

Some papers claimed for an improvement of the spatial resolution provided by systems using 256 channels [1,2], but the real increase of information has not been demon-strated yet, and it is controversial whether such a high number of channels only give raise to a useless spatial oversampling.

Measurement sessions using a high number of EEG channels are affected by several problems. Among these:

• High cost of the acquisition system and time-consuming mounting of the electrodes and uncomfortable condi-tions for the subjects undergoing the recordings. These issues are amplified when dealing with patients or with a pediatric population.

• Cumbersome management of the acquired data, high processing time and risk of overcrowding.

• Significant risk of replicated measurements for corrup-tion of the signal quality in some channels (i.e. sweat during physical activity, time-varying noise during the acquisition, etc.).

A few-channel system able to provide the same amount and quality of information obtained by a high-density one could be the solution for all the previous issues. The ideal system should use a limited amount of hardware (i.e., a few EEG channels) and some computational intelligence to derive the information generally acquired by the neglected channels. This approach can be pursued if the available recordings contain information on all (or at least most of) the independent sources of the cortical activity from which the electrical distribution over the scalp derives. After de-tecting the minimum acquisition set-up, in terms of both number and location of the electrodes, a computational model for the reconstruction of the cortical activity has to be designed and implemented.

In this pilot study, we developed a computational model where a neural approach has been adopted to extend the information provided by a minimum set of measurements to the entire scalp. This is achieved by automatically extract-ing the signals' features and by generalizextract-ing them in the space domain.

In the literature, Artificial Neural Networks (ANNs) have been extensively used for EEG analysis and classification. In particular, ANNs have been tested to automatically recognize normal and pathologic features [3,4], for the as-sessment of the anesthesia level [5], and also to solve the inverse electromagnetic problem [6].

In this study, different ANN models were analyzed to see if they could be used to reconstruct a whole set of EEG channels on the basis of a subset of them. In particular, three ANN architectures were designed, implemented and

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640 S. Conforto et al.

IFMBE Proceedings Vol. 41 compared. The minimal subset of EEG channels was

deter-mined by using an informative approach. The principle of the minimal complexity, in terms of both a-priori informa-tion (i.e. minimal input subset and training data set) and architecture of the model (i.e. topology, learning rule, train-ing algorithm) is the rationale followed in this study. The Occam’s razor (‘The simpler of two models, when both are consistent with the observed data, is to be preferred’) has been used for a final result adoptable in a real-life context.

This result is a neural model, driven by a reduced number of real measurements, which generates the correct cortical activity over the entire scalp (that is, it replicates the EEG traces recorded in disregarded locations over the scalp). The developed simplified system could be valuable in different fields, from the assessment of sport performance to the development of controllers for human-computer interfaces.

II.

M

ATERIALS AND METHODS

The neural minimum input model has been designed and implemented following this logical flow-chart: A. Data acquisition; B. Neural model design; C. Data set for training and testing; D. Model validation.

A. Data Acquisition

The EEG activity was recorded through surface elec-trodes placed over the scalp according to the International 10-20 System. Nineteen recording channels were acquired (0.05-50 Hz band-pass filtering followed by a 256 samples/s sampling) by a digital system (Micromed, Italy).

B. Neural Model Design

Minimum Input - The 19-channel recordings were

processed in order to extract the minimum set of significant channels to be used to drive the neural model. All record-ings underwent a Principal Component Analysis (PCA). The first principal component (PC) explained a variance ranging from 55% to 70%. A further part of the signal va-riance (15-25%) was explained by the second PC, while the other PCs were highly correlated to the recording noise.

8 channels were chosen as the most representative of the first two PCs by using a correlation measure together with a selection of the channels implementing the most uniform spatial sampling of the scalp. The channels respecting both criteria are: Fp1, Fp2, C3, C4, O1, O2, T3, T4.

ANN architecture – The architecture of the network was

assessed after implementing and comparing three different topologies characterized by 8 input neurons (i.e. the 8 chan-nels of the minimum input) and 11 output neurons (i.e. the 11 channels recorded in the data set but not included in the minimum input). The analyzed topologies are: 1) perceptron network (P_ANN) with no hidden layer, implementing a

mapping between 8 input samples and 11 output samples; 2) feed-forward network (F_ANN) with a 30-neurons hidden layer; 3) recurrent network (R_ANN) with a 30-neurons hidden layer and two 10-lags time delay lines (TDL) con-necting the input layer with the hidden one and the hidden layer with the output one respectively.

Training algorithm – All networks have been trained on

the same training set by using a supervised criterion imple-mented by the back-propagation algorithm.

C. Data Set for Training and Testing

The performance of the networks was assessed using dif-ferent EEG data sets from 5 normal subjects, 4 patients with generalized EEG abnormalities, and 3 patients with focal EEG abnormalities. Training and testing sets were obtained from these data sets as follows.

1. DS1: data recorded from a normal subject: 20 seconds for the training set and 20 seconds for the testing set;

2. DS2: data recorded from a patient. Generalized EEG abnormalities have been segmented to separate anomalous epochs from the normal ones. The training set included the normal epochs (20 seconds) and the testing set included the focal EEG abnormalities (20 seconds), to test the generali-zation properties of the network on a single patient basis;

3. DS3: data recorded from 5 normal subjects. These were used together to build a unique data set then subdi-vided into a training and a testing set, to test the generaliza-tion properties of the network with respect to different sub-jects. The training set includes 4 seconds of data from 4 subjects, and the testing set 9 seconds of data from the fifth subject.

4. DS4: data recorded from 2 normal subjects, 2 patients with generalized EEG abnormalities and 2 patients with focal EEG abnormalities have been used to build a training set. The corresponding testing set was obtained using the data from other participants to the experiment (i.e., 2 normal subjects, 2 patients with generalized EEG abnormalities and 1 patient with focal EEG abnormalities).

D. Model Validation

The models have been validated by means of the Mean Square Error (MSE) related to the reconstruction of the 11 EEG channels excluded from the minimum input set. In particular, the training curve has been studied in terms of MSE with respect to the training epochs. The MSE was calculated for the reconstruction of signals belonging to the testing set in both the time and the frequency domains.

The performance of the reconstruction was assessed by comparing the amplitude maps of the original data with those obtained from the data set including both the 8 origi-nal EEG data sets and the 11 EEG data sets reconstructed

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EFERENCES (1996) 256-chann Adjouadi M, Bar signals to orienta iomedical Science Y (2002) A multist and classification 557-1566. r G, Flyvbjerg H (2 G signals. IEEE T k WJ (2001) EEG patients. IEEE Tran RJ (2000) The forw ial neural networ o f Engineering, Univ erra 62 @uniroma3.it S. Confo at, in contrast an increasingly ub-set of them nalysis) allows tained with a la ). This result c ancy contained th the number S1. In this cas increases (even ude data from fferent EEG ac

als is good eno ned in this pilo gs seems suffi e scalp. The p a number of the implement Further invest s and to optim nstituting the m S

nel ERP informatio rreto A (2000) Re ation of visual stim es Instrumentation tage, multimethod of epileptiform E 2001) Automatic d rans on BME 48 ( complexity as a m ns on BME 48(12) ward EEG solutio rks. IEEE Trans

versity Roma TRE forto et al. with the y higher (in this s a func-arger set could be d in the of elec-e thelec-e relec-e- re-n if still m several ctivities. ough for ot study, icient to proposed applica-tation of tigations mize the minimum on growth. elating in-muli using 36:33-38. d approach EEG. IEEE differentia-1):111-6. measure of ):1424-33. ons can be on BME E