Presenting a Spatial-Geometric EEG Feature to Classify BMD and Schizophrenic Patients

Fatemeh AliMardani, Reza Boostani, Benjamin Blankertz


Schizophrenia (SZ) and bipolar mood disorder (BMD) patients demonstrate some similar signs and symptoms; therefore, distinguishing those using qualitative criteria is not an easy task especially when these patients experience manic or hallucination phases. This study is aimed at classifying these patients by spatial analysis of their electroencephalogram (EEG) signals. In this way, 22-channels EEG signals were recorded from 52 patients (26 patients with SZ and 26 patients with BMD). No stimulus has been used during the signal recording in order to investigate whether background EEGs of these patients in the idle state contain discriminative information or not. The EEG signals of all channels were segmented into stationary intervals called “frame” and the covariance matrix of each frame is separately represented in manifold space. Exploiting Riemannian metrics in the manifold space, the classification of sample covariance matrices is carried out by a simple nearest neighbor classifier. To evaluate our method, leave one patient out cross validation approach has been used. The achieved results imply that the difference in the spatial information between the patients along with control subjects is meaningful. Nevertheless, to enhance the diagnosis rate, a new algorithm is introduced in the manifold space to select those frames which are less deviated around the mean as the most probable noise free frames. The classification accuracy is highly improved up to 98.95% compared to the conventional methods. The achieved result is promising and the computational complexity is also suitable for real time processing.

Full Text:



American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders, 4th ed. American Psychiatric Association, Washington DC.

P. He, G. Wilson, C. Russell, M. Gerschutz, “Removal of ocular artifacts from the EEG: a comparison between time-domain regression and adaptive filtering method using simulated data”, Med. Biol. Eng. Comp. vol. 45, 2007, pp. 495–503.

A. Barachant, S. Bonnet, M. Congedo, C. Jutten, “Classification of covariance matrices using a Riemannian-based kernel for bci applications,” Neurocomputing, vol. 112, , 2013, pp. 172 – 178.

X. Pennec, P. Fillard, N. Ayache, “A Riemannian framework for tensor computing”, Int. J. Comput. Vis., vol. 66, no. 1, 2006, pp. 41–66.

A. Fuster, A. Tristan-Vega, TD. Haije, CF. Westin, L. Florack, “A Novel Riemannian Metric for Geodesic Tractography in DTI”, in Computational Diffusion MRI and Brain Connectivity Mathematics and Visualization, 2014, pp. 97-104.

J. H. Manton, “A globally convergent numerical algorithm for computing the centre of mass on compact Lie groups”, in Proc. of ICARCV, 2004, pp. 2211-2216.

A. Barachant, S. Bonnet, M. Congedo, C. Jutten, “Common spatial pattern revisited by Riemannian geometry”, In IEEE International Workshop on Multimedia Signal Processing (MMSP), 2010, pp. 472-476.

M. Marx, KB. Pauly, C. Chang, “A novel approach for global noise reduction in resting-state fMRI: APPLECOR”, Neuroimage, vol. 64, 2013, pp. 19–31.

F. Alimardani, R. Boostani, M. Azadehdel, A. Ghanizadeh, K. Rastegar, “Presenting a new search strategy to select synchronization values for classifying bipolar mood disorders from schizophrenic patients”, Engineering Applications of Artificial Intelligence, vol. 26, no. 2, 2013, pp. 913–923.

E. Parvinnia, M. Sabeti, M. Zolghadri Jahromi, R. Boostani, “Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm”, Journal of King Saud University – Computer and Information Sciences, vol.26, 2014, pp. 1–6 .

J. Chun, Z. N. Karam, F. Marzinzik, M. Kamali, L. O'Donnell, I .F. Tso, T. C. Manschreckd M. McInnis, P.J. Deldin, “Can P300 distinguish among schizophrenia, schizoaffective and bipolar I disorders? An ERP study of response inhibition”, Schizophrenia Research, vol. 151, no. 1–3, December 2013, pp. 175–184.

A. Barachant, S. Bonnet, M. Congedo, C. Jutten, “Riemannian geometry applied to BCI classification”, in Proc. 9th International Conference Latent Variable Analysis and Signal Separation (LVA/ICA),vol. 6365, Saint Malo-France, 2010, pp. 629-636.

A. Barachant, S. Bonnet, M. Congedo, C. Jutten, “Multi-class brain computer interface classification by Riemannian geometry”, IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, 2012, pp. 920-928.

F. Yger, “A review of kernels on covariance matrices for BCI applications”, IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Sep 2013, pp. 1-6.

A. Barachant, A. Andreev, M. Congedo, “The Riemannian Potato: an automatic and adaptive artifact detection method for online experiments using Riemannian geometry”, TOBI Workshop lV, Sion- Switzerland, 2013.

G. Pfurtscheller, C. Neuper, “Future prospects of ERD/ERS in the context of brain-computer interface (BCI) developments”, Progress in Brain Research, vol. 159, 2006, pp. 433–437.

F. Alimardani, R. Boostani, E. Ansari, “Feature selection SDA method in ensemble nearest neighbor classifier”, In Proc. of the Springer, 13th International Conference of Computer Science and Engineering, Kish- Iran, March 2008, pp. 9–11.



  • There are currently no refbacks.