The development of brain-computer interfaces (BCIs) for disabled patients is currently a growing field of research. Most BCI systems are based on electroencephalography (EEG) signals, and within this group, systems using motor imagery (MI) are amongst the most flexible. However, for stroke patients, the motor areas of the brain are not always available for use in these types of devices. The aim of this work was to evaluate a set of imagery-based cognitive tasks (right-hand MI versus music imagery, with rest or 'blank' periods in between), using functional Magnetic Resonance Imaging (fMRI) and EEG. Eleven healthy subjects (control group) and four stroke patients were evaluated with fMRI, and nine of the healthy subjects also underwent an EEG test. The fMRI results for the control group showed specific and statistically differentiable activation patterns for motor versus music imagery (t-test, p < 0.001). For stroke patients the fMRI results showed a very large variability, with no common activation pattern for either of the imagery tasks. Corroborating this fact, EEG results concerning feature selection for minimizing the classification error (using the Davies-Bouldin index) have also found no common activation pattern, although a well-defined set of meaningful electrodes and frequencies was found for some subjects. In terms of classification performance using EEG data, this work has detected a group of subjects with classifier rate of success up to 60%, which is promising in view of the cognitive complexity of the adopted tasks. © 2013 IEEE. Soekadar, S., Birbaumer, N., Cohen, L.G., Brain-computer-interfaces in the rehabilitation of stroke and neurotrauma (2011) Systems Neuroscience and Rehabilitation, pp. 3-18. , chapter 1, K Kansaku and LG Cohen, Eds. Tokyo: Springer JapanDaly, J.J., Wolpaw, J.R., Brain-computer interfaces in neurological rehabilitation (2008) Lancet Neurology, 7 (11), pp. 1032-1043Gomez-Rodriguez, M., Grosse-Wentrup, M., Hill, J., Gharabaghix, A., Schölkopf, B., Peters, J., Towards brain-robot interfaces in stroke rehabilitation (2011) IEEE 12th International Conference on Rehabilitation Robotics, , SwitzerlandNicolas-Alonso, L., Gomez-Gil, J., Brain computer interfaces, a review (2012) Sensors, 12, pp. 1211-1279Neuper, C., Scherer, R., Wriessnegger, S., Pfurtschelle, G., Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain-computer interface (2009) Clinical Neurophysiology, 120, pp. 239-247Berman, B., Horovitz, S., Venkataraman, G., Hallett, M., Selfmodulation of primary motor cortex activity with motor and motor imagery tasks using real-time fMRI-based neurofeedback (2012) Neuroimage, 59 (2), pp. 917-925Birch, G., Bozorgzadeh, Z., Mason, S., Initial on-line evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials (2002) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 10 (4), pp. 219-224Formaggio, E., Storti, S., Cerini, R., Fiaschi, A., Manganotti, P., Brain oscillatory activity during motor imagery in EEG-fMRI coregistration (2010) Magnetic Resonance Imaging, 28, pp. 1403-1412Halder, S., Agorastos, D., Veit, R., Hammer, E.M., Lee, S., Varkuti, B., Neural mechanisms of brain-computer interface control (2011) Neuro Image, 55, pp. 1779-1790Hermes, D., Vansteensel, M.J., Albers, A.M., Bleichner, M.G., Benedictus, M.R., Mendez, C., Functional MRI-based identification of brain areas involved in motor imagery for implantable brain-computer interfaces (2011) Journal of Neural Engineering, 8, p. 025007McFarland, D.J., Miner, L.A., Vaughan, T.M., Wolpaw, J.R., Mu and beta rhythm topographies during motor imagery and actual movements (2000) Brain Topography, 12 (3), pp. 177-186Zatorre, R.J., Halpern, A.R., Mental concerts: Musical imagery and auditory cortex (2005) Neuron, 47, pp. 9-12Schaefer, R.S., Farquhar, J., Blokland, Y., Sadakata, M., Desain, P., Name that tune: Decoding music from the listening brain (2011) Neuro Image, 56, pp. 843-849Schaefer, R.S., Vlek, R.J., Desain, P., Music perception and imagery in EEG: Alpha band effects of task and stimulus (2011) International Journal of Psychophysiology, 82, pp. 254-259Schaefer, R.S., Vlek, R.J., Desain, P., Decomposing rhythm processing: Electroencephalography of perceived and self-imposed rhythmic patterns (2011) Psychological Research, 75, pp. 95-106Vlek, R.J., Schaefer, R.S., Gielen, C.C.A.M., Farquhar, J.D.R., Desain, P., Shared mechanisms in perception and imagery of auditory accents (2011) Clinical Neurophysiology, 122, pp. 1526-1532Schaefer, R.S., Blokland, Y., Farquhar, J., Desain, P., Single trial classification of perceived and imagined music from EEG (2009) Berlin BCI Workshop, , Berlin, GermanyVlek, R.J., Schaefer, R.S., Gielen, C.C.A.M., Farquhar, J.D.R., Desain, P., Sequenced subjective accents for brain-computer interfaces (2011) Journal of Neural Engineering, 8, p. 11. , Article ID 036002Klonowski, W., Duch, W., Perovic, A., Jovanovic, A., Some computational aspects of the brain computer interfaces based on inner music (2009) Computational Intelligence and Neuroscience, 2009, p. 9. , Article ID 950403Miranda, E.R., Magee, W.L., Wilson, J.J., Eaton, J., Palaniappan, R., Brain-computer music interfacing (BCMI): From basic research to the real world of special needs (2011) Music and Medicine, 3 (3), pp. 134-140Davies, D.L., Bouldin, D.W., A cluster separation measure (1979) IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1 (2), pp. 224-227McFarland, D.J., McCane, L.M., David, S.V., Wolpaw, J.R., Spatial filter selection for EEG-based communication (1997) Electroencephalography and Clinical Neurophysiology, 103, pp. 386-394Dornhege, G., Millán, J.R., Hinterberger, T., McFarland, D., Müller, K.-R., (2007) Toward Brain Computer Interfacing, , The MIT PressWolpaw, J., Wolpaw, E.W., (2012) Brain-Computer Interfaces: Principles and Practice, , Oxford University PressCoelho, G.P., Barbante, C.C., Boccato, L., Attux, R.R.F., Oliveira, J.R., Von Zuben, F.J., Automatic feature selection for BCI: An analysis using the davies-bouldin index and extreme learning machines (2012) Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), , Brisbane, Australia