Biomedical subjects

Florian Losch

Publications and source records attributed to Florian Losch.

5 recordsLinked to original sources

PubMed2006

Handwriting performance in the absence of visual control in writer's cramp patients: initial observations.

BACKGROUND: The present study was aimed at investigating the writing parameters of writer's cramp patients and control subjects during handwriting of a test sentence in the absence of visual control. METHODS: Eight right-handed patients with writer's cramp and eight healthy volunteers as age-matched control subjects participated in the study. The experimental task consisted in writing a test sentence repeatedly for fifty times on a pressure-sensitive digital board. The subject did not have visual control on his handwriting. The writing performance was stored on a PC and analyzed off-line. RESULTS: During handwriting all patients developed a typical dystonic limb posture and reported an increase in muscular tension along the experimental session. The patients were significantly slower than the controls, with lower mean vertical pressure of the pen tip on the paper and they could not reach the endmost letter of the sentence in the given time window. No other handwriting parameter differences were found between the two groups. CONCLUSION: Our findings indicate that during writing in the absence of visual feedback writer's cramp patients are slower and could not reach the endmost letter of the test sentence, but their level of automatization is not impaired and writer's cramp handwriting parameters are similar to those of the controls except for even lower vertical pressure of the pen tip on the paper, which is probably due to a changed strategy in such experimental conditions.

Adult↗

PubMed2006

Combined optimization of spatial and temporal filters for improving brain-computer interfacing.

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.

Algorithms↗

PubMed2006

The Berlin Brain-Computer Interface: EEG-based communication without subject training.

The Berlin Brain-Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are 1) the use of well-established motor competences as control paradigms, 2) high-dimensional features from 128-channel electroencephalogram (EEG), and 3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left- versus right-hand movements in healthy subjects. A more recent study showed that the RP similarily accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach, oscillatory features are used to discriminate imagined movements (left hand versus right hand versus foot). In a recent feedback study with six healthy subjects with no or very little experience with BCI control, three subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems.

Algorithms↗

PubMed2005

Electroencephalographic spectral power in writer's cramp patients: evidence for motor cortex malfunctioning during the cramp.

We investigated cortical activation as reflected in task-related spectral power (TRPow) changes in 8 writer's cramp patients during writing on a digital board and during isometric contraction and compared them to those of 8 age-matched healthy subjects. Scalp EEG was recorded over the contralateral primary sensorimotor area (SM1(c)), and from the ipsilateral sensorimotor area (SM1(i)). The electromyogram (EMG) was recorded from the Extensor Digitorum Communis (Extensor), Flexor Digitorum Superficialis (Flexor), and First Dorsal Interosseous (FDI) muscles. We analyzed (1) handwriting performance, (2) changes in the TRPow confined to alpha and beta band, and (3) the EMG spectral power during both tasks, writing and isometric contraction. During writing, all patients developed writer's cramp. The handwriting in writer's cramp patients was associated with significantly less reduction of the beta-range TRPow and lower frequency of the TRPow reduction compared to controls. No significant differences between patients and controls for the alpha band TRPow reduction during handwriting were observed. During writing, the patients showed higher EMG spectral power than the controls but this difference was at the border of significance. The present results indicate disorder in the motor execution system, in writer's cramp patients, associated with impaired functional beta-network state of the contra- and ipsilateral sensorimotor cortices, most probably due to inadequate modulation of the intracortical inhibition associated with writing.

Adult↗

PubMed2003

Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis.

Brain-computer interfaces (BCIs) involve two coupled adapting systems--the human subject and the computer. In developing our BCI, our goal was to minimize the need for subject training and to impose the major learning load on the computer. To this end, we use behavioral paradigms that exploit single-trial EEG potentials preceding voluntary finger movements. Here, we report recent results on the basic physiology of such premovement event-related potentials (ERP). 1) We predict the laterality of imminent left- versus right-hand finger movements in a natural keyboard typing condition and demonstrate that a single-trial classification based on the lateralized Bereitschaftspotential (BP) achieves good accuracies even at a pace as fast as 2 taps/s. Results for four out of eight subjects reached a peak information transfer rate of more than 15 b/min; the four other subjects reached 6-10 b/min. 2) We detect cerebral error potentials from single false-response trials in a forced-choice task, reflecting the subject's recognition of an erroneous response. Based on a specifically tailored classification procedure that limits the rate of false positives at, e.g., 2%, the algorithm manages to detect 85% of error trials in seven out of eight subjects. Thus, concatenating a primary single-trial BP-paradigm involving finger classification feedback with such secondary error detection could serve as an efficient online confirmation/correction tool for improvement of bit rates in a future BCI setting. As the present variant of the Berlin BCI is designed to achieve fast classifications in normally behaving subjects, it opens a new perspective for assistance of action control in time-critical behavioral contexts; the potential transfer to paralyzed patients will require further study.

Algorithms↗