Towards Zero Training Figure 1. Sessions 1 to N-1 show a standard BCI procedure: spatial filter and classifiers are learned each session anew from a calibration recording (e.g. with CSP and LDA) before they are applied during a feedback application. The new Zero-Training method eliminates the calibration recording: spatial filters and a classifier are predetermined before session N starts. The spatial filters for session N are extracted from old spatial filters (blue), the classifier for session N is calculated from old calibration recordings (red). The feedback application of session N is preceded only by a very quick bias adaptation (yellow). doi:10.1371/journal.pone.0002967.g001 new BCI session. In the following, we introduce an experimental setting that allows for the comparison of the Zero-Training approach with the ordinary approach including calibration. Finally, we show the results of this comparison, discuss our findings and end with a conclusion. Methods 1. Background A. Neurophysiology. Macroscopic brain activity during resting wakefulness contains distinct ‘idle’ rhythms located over various brain areas. Sensorimotor cortices show rhythmic macroscopic EEG oscillations (m-rhythm or sensorimotor rhythm, SMR), with spectral peak energies of about 8–14 Hz (a- band) and/or 16–28 Hz (b-band) localized in somatosensory cortex [6]. A large class of EEG-based BCI systems relies on the fact that amplitude modulations of sensorimotor rhythms can be caused, e.g. by imagining movements. For example, the power of the m- rhythm decreases during imagined hand movements in the corresponding representation area which is located in the contralateral sensorimotor cortex. This phenomenon is called event-related desynchronization (ERD, [7,8]), while the increase of band power is termed event-related synchronization (ERS). This may be observed, e.g., during motor imagery over flanking sensorimotor areas, possibly reflecting an ‘surround inhibition’ enhancing focal cortical activation, see [9,8]. The exact location and the exact frequency band of the sensorimotor rhythm is subject-specific. Hence individually optimized filters can increase the signal-to-noise ratio dramatically [10]. To this end, the CSP technique has proven to be useful. B. Common Spatial Pattern (CSP) Analysis. Common Spatial Pattern and its extensions (e.g. [11,12,13,14,10]) is a technique to analyze multi-channel data based on recordings from two classes (conditions). It is, e.g. used in BCI systems based on the modulation of brain rhythms. CSP filters maximize the EEG signal’s variance under one condition while simultaneously minimizing it for the other condition. Since variance of band- pass filtered signals is equal to band power, CSP analysis is applied to band-pass filtered signals in order to obtain an effective discrimination of mental states that are characterized by ERD/ ERS effects (see above). In the example of left vs. right hand motor imagery, the CSP algorithm will find two groups of spatial filters. The first will show high band power during left hand motor imagery and low band power during right hand motor imagery, and the second vice versa. Let Si be the covariance matrix of the trial-concatenated matrix of dimension [C6T] (where C is the number of electrodes and T is the number of concatenated samples) belonging to the respective class iM{1,2}. The CSP analysis consists of calculating a matrix W [RC|C and a diagonal matrix D with elements in [0,1] such that W tS1W ~D and W tS2W ~I {D 1 ð Þ where I [RC|C is the identity matrix. This can be solved as a generalized eigenvalue problem. The projection that is given by PLoS ONE | www.plosone.org 2 August 2008 | Volume 3 | Issue 8 | e2967