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
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Department of Engineering, University Roma TRE, Rome, Italy 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 progressive increase in the number of channels for EEG recording. 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 derived from a standard 19 channel EEG recording was obtainable 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 supervised 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 examples 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 8channel 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 reconstructed channels and the applicability of the approach in reallife contexts. Keywords—Artificial Neural Network, EEG, Spectral Analysis, Time Analysis, Amplitude Maps.
I. INTRODUCTION 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 demonstrated 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:
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High cost of the acquisition system and time-consuming mounting of the electrodes and uncomfortable conditions 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 corruption 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 detecting 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 extracting the signals' features and by generalizing 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 assessment 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
L.M. Roa Romero (ed.), XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, IFMBE Proceedings 41, DOI: 10.1007/978-3-319-00846-2_158, © Springer International Publishing Switzerland 2014
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compared. The minimal subset of EEG channels was determined by using an informative approach. The principle of the minimal complexity, in terms of both a-priori information (i.e. minimal input subset and training data set) and architecture of the model (i.e. topology, learning rule, training 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. MATERIALS 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 electrodes 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 recordings 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 variance (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 channels 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) connecting 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 implemented by the back-propagation algorithm. C. Data Set for Training and Testing The performance of the networks was assessed using different 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 generalization 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 subdivided into a training and a testing set, to test the generalization properties of the network with respect to different subjects. 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 original EEG data sets and the 11 EEG data sets reconstructed
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A Neural N Minimum m Input Model too Reconstruct thhe Electrical Corttical Activity
witth the ANN. The T maps undeertook a blind evaluation e by a poool of expert neeurologists.
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n whho analyzed thhe reconThe paanel of expert neurologists structed traces t and relaative spectra poositively evaluuated the quality off the reconstrucction.
III. RESU ULTS After training with DS1, all the designedd ANNs reconstruuct the 11 negglected channeels with a globbal MSE lowerr theen 1*10-2. The reconstructionn was perform med sample-bysam mple, and the obtained resullts show the inndependence of thee signal tracess from their time course. Thhis feature hass beeen demonstrateed also on a surrogate s versiion of the dataa setts obtained by time t shuffling the data. With regard too DS1, its surroogate and the ANNs: A •
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P_ANN recoonstructed the channel F3 with w the minimum error (MSE= ( 2.4*10-3 ), and the chhannel F8 withh the worst perrformance (MS SE= 1*10-2). F_ANN recoonstructed the channel F3 with w the minimum error (MSE= ( 2.4*10-3 ), and the chhannel F8 withh the worst perrformance (MS SE= 9.5*10-3). R_ANN show wed the minim mum error for the channel F33 (MSE= 5*100-3) and the woorst performannce for channel F4 (MSE= 1*10-2).
On the basis of these resultts, we choose the model im-pleemented by P_ANN, becauuse of its sim mplicity and itss perrformance leveel, comparablee to that of thee other modelss. Thhe reconstruction results in the time and frequency domaains are compaared with the orriginal data in Figure F 1. From now on,, the results reffer to the perfoormance of thee perrceptron P_AN NN after traininng with DS2, DS3, D DS4. DS2 - The traaining reached convergence in 400 epochss, witth MSE=0.0022. The reconstrruction MSE iss minimum forr F4 (MSE=3.9*100-3) and maximuum for F7 (MS SE=2.1*10-2). DS3 - The traaining reached convergence in 100 epochss, witth MSE=0.0033. The reconstrruction MSE iss minimum forr P4 (MSE=7.8*100-3) and maximuum for T6 (MS SE=1.8*10-2). DS4 - The traiining exceeds 400 4 epochs witth MSE=0.001. Thhe reconstructioon MSE was evvaluated usingg three differennt tessting sets: norm mal data, genneralized EEG abnormalitiess, foccal EEG abnorm malities. For the normaal testing dataa, the reconstruuction MSE iss minimum for P44 (MSE=3.3*10 1 -3) and maxximum for C33 -2 (M MSE=1*10 ). For the generralized EEG abbnormalities, the t reconstruction MSE is minnimum for P44 (MSE=1.6*100-3) and maximuum for P3 (MSE=1.1*10-2). For the focal EEG E abnormallities, the reconnstruction MSE E is minimum for F3 (MSE=7.33*10-3) and maaximum for T66 (M MSE=1.7*10-2). The quality of o the reconstruucted T6 channell in the time and a frequency domains withh respect to thee oriiginal data can be appreciatedd from Figure 2. 2
Fig. 1 Resuults obtained by P__ANN trained by DS1: D F3 is the bestt and F8 is the worst reconstructed r channnel, respectively. The T reconstructed channels are co ompared with the original o data in botth time and frequeency.
Fig. 2 Resuults obtained by P__ANN trained by DS4: D F3 is the bestt and T6 is the worst reconstructed r channnel, respectively. The T reconstructed channels are co ompared with the original o data in botth time and frequeency.
Amplitudee Maps - The P_NN trainedd and tested wiith DS4, chosen ass the most com mplex and genneral set of daata, was used to reeconstruct the amplitude mapps of the 11 neglected n channels, and to compaare them with the same mapps calcuh the original data. d The panell of expert neurrologists lated with defined the t reconstructtions acceptable and promissing. An example of these mapss is shown inn Figure 3. Am mplitude maps aree calculated froom the original data (upperr panel), and from the 8 originall channels and 11 reconstructted ones (lower paanel, reconstruccted traces in reed). IV. CONCLUSION NS The deesigned neural models (P_AN NN, F_ANN, R_ANN) R have the same performance level wheen tested on DS1 D data t reasons P_ANN P was chhosen as the model m to set. For these be preferrred because off its simple topoology. Then, P_ANN was used u to explorre the behavioor of the model in data set withh increased gennerality, such as DS2,
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These preliminary reesults show thaat, in contrast with the d EEG systems with an a increasinglyy higher trend to develop number of o channels, a significant suub-set of them (in this study, thee 8 obtained by b the PCA annalysis) allowss a functional anaalysis that is eqquivalent to obttained with a laarger set of channeels (i.e. the alll 19 channels)). This result could c be explained d by an inform mation redundaancy containedd in the acquired signals, whichh increases witth the number of electrodes positioned over thhe scalp. The redundancy is veery high for DS1. In this case the reconstructiion MSE is very low, but increases i (evenn if still acceptablle) when the data d sets incluude data from m several patients/ssubjects characcterized by diffferent EEG acctivities. The qualiity of the reconnstructed signaals is good enoough for the routin ne clinical analyysis. From the t preliminaryy results obtainned in this piloot study, the use a minimum seet of recordinggs seems suffiicient to reconstruct the EEG acctivity over thee scalp. The proposed p approach thus seems promising p for a number of applications, and d opens new scenarios s for the t implementtation of smart neu ural computerr-interfaces. Further investtigations are needeed to deeply test t the models and to optim mize the choice off the sub-set off channels connstituting the minimum m input set for f the networkk.
REFERENCESS Figg. 3 Amplitude maaps extracted at 4 time t instants by a 19-channel 1 record--
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ingg extracted from thhe data set. M aps are calculated from m the original dataa (uupper panel), and from f the 8 originall and the 11 reconsstructed channels (low wer panel, reconstruucted traces in redd).
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DS S3, DS4. In parrticular, DS2 aims a at testing the generalization capability of o the model to t recognize normal n and abnorrmal EEG activvities on a singgle patient basiis. The P_ANN N is trained througgh 400 epochs and the perfoormance on thee tessting set is acceeptable. The training process becoomes faster for f DS3 (1000 epoochs), which inncludes data obbtained from different normaal subbjects. The P__ANN seems to specialize in recognizingg norrmal activity (training fast and correct) but b has poorerr perrformance thann the P_ANN trained by DS S2 on the testingg set. This coulld be due to som me form of overtraining. The most impportant result is i the one obttained with thee DS S4, which is the most genneral data set. In this casee, P__ANN perform ms well on botth training and testing dataa. Sinnce testing dataa are composeed by different EEG activitiess andd the performaance is equivallent for all the testing set, thee moodel generalizees well over diifferent subjectts and differennt acttivities, giving rise to a promiising result.
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Pfliegeer ME, Sands SF (1996) 256-channnel ERP informatioon growth. Neuroiimage 3.3: S10. Suarezz E, Viegas MD, Adjouadi M, Barrreto A (2000) Reelating induced changes in EEG signals to orientaation of visual stim muli using the ES SI-256 machine. Biiomedical Sciencees Instrumentation 36:33-38. Liu HS S, Tong Z, Fu SY Y (2002) A multisttage, multimethodd approach for auttomatic detection and classification of epileptiform EEG. E IEEE Trans on BME 49(12):15557-1566. Peters BO, Pfurtschellerr G, Flyvbjerg H (22001) Automatic differentiad tion off multichannel EEG G signals. IEEE Trans on BME 48 (1):111-6. Zhang XS, Rob JR, Erikk WJ (2001) EEG complexity as a measure m of depth of o anesthesia for patients. p IEEE Tranns on BME 48(12)):1424-33. Sun M and Sclabassi RJ R (2000) The forw ward EEG solutioons can be compu uted using artificiial neural networrks. IEEE Trans on BME 47(8):1044-50. Authorr: Silvia Confortoo Institu ute: Department off Engineering, Univversity Roma TRE E Street: Via Vito Volteerra 62 Rome City: Countrry: Italy Email::
[email protected]
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