Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship.

Katarzyna Blinowska

Gernot Müller-Putz

Vera Kaiser

Laura Astolfi

Katrien Vanderperren

Sabine Van Huffel

Louis Lemieux

Multimodal Imaging of Human Brain Activity: Rational, Biophysical Aspects and Modes of Integration

The performance of spatial filters based on independent components analysis (ICA) was evaluated by employing principal component analysis (PCA) preprocessing for dimensional reduction. The PCA preprocessing was not found to be a suitable method that could retain motor imagery information in a smaller set of components. In contrast, 6 ICA components selected on the basis of visual inspection performed comparably (61.9%) to the full range of 22 components (63.9%). An automated selection of ICA components based on a variance criterion was also carried out. Only 8 components chosen this way performed better (63.1%) than visually selected components. A similar analysis on the reduced set of electrodes over mid-central and centro-parietal regions of the brain revealed that common spatial patterns (CSPs) and Infomax were able to detect motor imagery activity with a satisfactory accuracy.

Muhammad Naeem

Clemens Brunner

Gert Pfurtscheller

Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data

The paper describes  a  framework for efficient sharing of knowledge between research groups, which have been working for several years without flaws. The obstacles in cooperation are connected primarily with the  lack of platforms for effective exchange of experimental data, models,  and algorithms. The solution to these problems is proposed by  construction of the platform (EEG.pl) with the semantic aware search scheme between portals. The above approach implanted in the international cooperative projects like NEUROMATH may bring the significant progress in designing  efficient methods for neuroscience research.

P. J. Durka

G. J. Blinowski

H. Klekowicz

U. Malinowska

R. Ku&#x15b;

K. J. Blinowska

Information Infrastructure for Cooperative Research in Neuroscience

We discuss the BCI based on inner tones and inner music. We had some success in the detection of inner tones, the imagined tones which are not sung aloud. Rather easily imagined and controlled, they offer a set of states usable for BCI, with high information capacity and high transfer rates. Imagination of sounds or musical tunes could provide a multicommand language for BCI, as if using the natural language. Moreover, this approach could be used to test musical abilities. Such BCI interface could be superior when there is a need for a broader command language. Some computational
estimates and unresolved difficulties are presented.

Wlodzimierz Klonowski

Wlodzis&#x142;aw Duch

Aleksandar Perovic

Aleksandar Jovanovic

Some Computational Aspects of the Brain Computer Interfaces Based on Inner Music

EEG-based discrimination between different motor imagery states has been subject of a number of studies in healthy subjects. We investigated the EEG of 15 patients with complete spinal cord injury during imagined right hand, left hand, and feet movements. In detail we studied pair-wise discrimination functions between the 3 types of motor imagery. The following classification accuracies (mean <svg     style="vertical-align:-0.3003pt;width:10.1px;" id="M1" height="9.5625" version="1.1" viewBox="0 0 10.1 9.5625" width="10.1"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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</svg> SD) were obtained: left versus right hand <svg     style="vertical-align:-0.4368pt;width:101.0375px;" id="M2" height="11.75" version="1.1" viewBox="0 0 101.0375 11.75" width="101.0375"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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</svg>, left hand versus feet <svg     style="vertical-align:-0.4368pt;width:109.2px;" id="M3" height="11.75" version="1.1" viewBox="0 0 109.2 11.75" width="109.2"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.15)"><use xlink:href="#x36"/></g><g transform="matrix(.017,-0,0,-.017,8.222,11.15)"><use xlink:href="#x38"/></g><g transform="matrix(.017,-0,0,-.017,16.381,11.15)"><use xlink:href="#x2E"/></g><g transform="matrix(.017,-0,0,-.017,20.257,11.15)"><path id="x31" d="M384 0h-275v27q67 5 81.5 18.5t14.5 68.5v385q0 38 -7.5 47.5t-40.5 10.5l-48 2v24q85 15 178 52v-521q0 -55 14.5 -68.5t82.5 -18.5v-27z" /></g><g transform="matrix(.017,-0,0,-.017,28.416,11.15)"><path id="x39" d="M244 635q90 0 143 -72t53 -177q0 -133 -65 -229.5t-171 -139.5q-79 -32 -140 -32l-5 30q109 18 185 91t101 186l-68 -36q-29 -16 -60 -16q-79 0 -129 51.5t-50 130.5q0 80 57 146.5t149 66.5zM228 602q-52 0 -78 -45.5t-26 -98.5q0 -69 36.5 -115.5t97.5 -46.5&#xA;q53 0 90 28q4 31 4 66q0 51 -9.5 95.5t-39 80.5t-75.5 36z" /></g><g transform="matrix(.017,-0,0,-.017,36.576,11.15)"><use xlink:href="#x25"/></g><g transform="matrix(.017,-0,0,-.017,56.022,11.15)"><use xlink:href="#xB1"/></g><g transform="matrix(.017,-0,0,-.017,72.613,11.15)"><use xlink:href="#x31"/></g><g transform="matrix(.017,-0,0,-.017,80.773,11.15)"><use xlink:href="#x31"/></g><g transform="matrix(.017,-0,0,-.017,88.932,11.15)"><use xlink:href="#x2E"/></g><g transform="matrix(.017,-0,0,-.017,92.808,11.15)"><use xlink:href="#x30"/></g><g transform="matrix(.017,-0,0,-.017,100.967,11.15)"><use xlink:href="#x38"/></g>

		
	
</svg>, and right hand versus feet <svg     style="vertical-align:-0.4368pt;width:101.0375px;" id="M4" height="11.75" version="1.1" viewBox="0 0 101.0375 11.75" width="101.0375"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,11.15)"><use xlink:href="#x36"/></g><g transform="matrix(.017,-0,0,-.017,8.222,11.15)"><use xlink:href="#x35"/></g><g transform="matrix(.017,-0,0,-.017,16.381,11.15)"><use xlink:href="#x2E"/></g><g transform="matrix(.017,-0,0,-.017,20.257,11.15)"><use xlink:href="#x30"/></g><g transform="matrix(.017,-0,0,-.017,28.416,11.15)"><use xlink:href="#x35"/></g><g transform="matrix(.017,-0,0,-.017,36.576,11.15)"><use xlink:href="#x25"/></g><g transform="matrix(.017,-0,0,-.017,56.022,11.15)"><use xlink:href="#xB1"/></g><g transform="matrix(.017,-0,0,-.017,72.613,11.15)"><use xlink:href="#x39"/></g><g transform="matrix(.017,-0,0,-.017,80.773,11.15)"><use xlink:href="#x2E"/></g><g transform="matrix(.017,-0,0,-.017,84.648,11.15)"><use xlink:href="#x32"/></g><g transform="matrix(.017,-0,0,-.017,92.808,11.15)"><use xlink:href="#x35"/></g>

		
	
</svg>. In 5 out of 8 paralegic patients, the discrimination accuracy was greater than 70&#x25; but in only 1 out of 7 tetraplagic patients. The present findings provide evidence that in the majority of paraplegic patients an EEG-based BCI could achieve satisfied results. In tetraplegic patients, however, it is expected that extensive training-sessions are necessary to achieve a good BCI performance at least in some subjects.

G. Pfurtscheller

P. Linortner

R. Winkler

G. Korisek

G. Müller-Putz

Discrimination of Motor Imagery-Induced EEG Patterns in Patients with Complete Spinal Cord Injury

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<p><em>Computational Intelligence and Neuroscience</em> is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.</p>


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Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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