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The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.
Musical performance is a highly complex and demanding challenge for the human brain  — . For example, a pianist playing a Beethoven sonata has to retrieve from memory which notes have to be played, and in which order this has to be done. Then, the corresponding motor programs have to be activated in order to execute the right movements at the right time with the right intensity. Last but not least, the pianist permanently has to monitor and evaluate the effects of the executed actions for correctness. Importantly, all the processes are constantly overlapping in time. Even though the pianist tries to avoid errors like hitting the wrong key, such errors nevertheless occasionally occur.
One question that arises in the context of any kind of motor expertise in our case piano playing is at what point in time errors are actually detected by the sensorimotor system. More specifically, in the present study we investigated whether errors are detected before a movement is fully executed. In the motor control literature, it is assumed that fast movement sequences are controlled without external feedback, because the delays of sensory feedback are too long to have an impact on performance for a review, see .
Analysis of NoC Error Recovery Schemes
Accordingly, studies in the music domain showed that auditory feedback is not a prerequisite for a successful performance  —  , for a review, see . These studies found that the complete absence of feedback has mostly no effects on piano performance whereas specific alterations of auditory feedback can profoundly disrupt performance, see  —  , . Hence, it seems possible that monitoring mechanisms in pianists can operate without auditory feedback, i.
Furthermore, a behavioral study tried to investigate whether motor experts can detect errors before the movement is completed . That study found that incorrect responses of expert typists were less forceful than correct responses. However, it is not clear whether this effect reflects error-specific processing or results from less activation of the incorrect response see e.
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In addition, no real-time correlate of electrical brain activity e. Recording EEG is a technique particularly suited to investigate the time course of cognitive processes on a fine-grained time-scale, as for example the time an error is detected. EEG-studies on error processing for reviews, see  —  isolated a component of the event-related potential ERP appearing shortly after participants commit an error in a variety of speeded response tasks termed the error-related negativity, ERN or Ne  , . Participants were presented with sequences of word pairs with identical initial phonemes e.
Every few trials, a word pair was marked for overt articulation. When participants are required to vocalize those last word pairs, they are likely to commit errors e. This study  found an increased negativity after the presentation of the last word pair, and a second negativity after the presentation of the vocalization prompt. However, it remained unclear when exactly participants started to produce speech, and hence the timing of this error response is not evident. Furthermore, participants saw in each trial the stimuli that induced conflict and hence the speech errors.
Therefore, the observed ERP effect might have reflected the resolution of conflict in erroneous trials, rather than the detection of an upcoming error. Thus, neural correlates of error detection prior to error execution have remained elusive. In the present study we investigated expert pianists performing from memory while we recorded the EEG. That is, we investigated highly trained experts committing errors in a complex situation, in which participants did not react to external conflict-inducing stimuli. We compared the brain potentials before and after correct and incorrect keystrokes.
More specifically, we hypothesized that differences in the ERP pattern of correct and incorrect keystrokes would occur even before the completion of the movement. Ten highly trained pianists 6 female; mean age Participants had on average All participants were right-handed according to the Edinburgh Handedness Inventory  mean laterality quotient: The study was approved by the local ethics committee of the University of Leipzig, and conducted in accordance with the Declaration of Helsinki.
The stimuli consisted of major scales and two similar scale-like patterns in two voices see Figure 1. The order of blocks was randomized with the constraints that no identical stimulus type scale, pattern A, pattern B occurred in direct succession and that stimuli with the same order of major keys occurred maximally two times in direct succession. A Pattern A in C-Major. The instructed tempo for the scales was beats per minute bpm and for the patterns 69 bpm, i. Randomly between every 40th to 60th produced note, the auditory feedback of a single note was manipulated by lowering the pitch of one note by one semitone.
The results of that manipulation will be reported elsewhere. The pianists performed on a Yamaha digital piano Clavinova CLP , and listened to their performances via AKG studio headphones at comfortable listening levels approximately 65 dB, dependent on the velocity of a keypress. In the first part of the experiment ca.
Following a practice period with the notation in front of them, participants were blindfolded to exclude visual feedback and to increase the task difficulty and instructed to reproduce these stimuli bimanually parallel in octaves in the same tempo as they heard them before, i.
If they were not able to perform in the same tempo, they chose their fastest possible tempo. They were informed about the feedback manipulations, and instructed to continue playing, in the event of a feedback manipulation as well as a mistake. When required, participants could rest between two blocks. Before each block, an acoustic instruction was played, informing the participants which scales or patterns they had to produce in the following block. Each performance session lasted approximately 1. Testing was carried out in an acoustically and electrically shielded EEG cabin.
To synchronize musical and electrophysiological data, this program sent trigger signals concurrently with every 5th keypress and concurrently with the feedback manipulations to the EEG acquisition computer. For offline analyses, the MIDI information including timing information, keypress velocities, and pitch was saved on a hard disk. The ground electrode was placed on the sternum. EEG signals were digitized with a sampling frequency of Hz.
After data acquisition, EEG data were downsampled to Hz to reduce the data size and re-referenced to the arithmetical mean of both mastoid electrodes. After calculating the independent components, artifactual components due to eye movements and blinks were selected based on the following criteria: a component was considered to be artifactual if its topography showed peak activity only over the horizontal or vertical eye electrodes, if it showed a smoothly decreasing power spectrum which is typical for eye movement artifacts, see  , and if the component's activity contributed mainly to the raw EEG signal recorded by the horizontal and vertical eye electrodes.
Performance errors were defined as playing an incorrect key with one hand while pressing the correct key with the other hand. Errors were manually identified off-line. Epochs containing other types of errors like omissions or incorrect keypresses with both hands simultaneously were discarded on average, there were only 18 trials per participant containing the latter type of error.
Only errors that were preceded by a 1 s period of error-free performance and free of feedback manipulations were analyzed. Errors were identified separately for the scales and the patterns to take into consideration that the different tempi of both types of stimuli possibly influenced ERP effects. On average, there were only 9 error trials during the performance of the scales, which is insufficient to obtain a reasonable signal-to-noise ratio.
Therefore, these data were discarded and we will thus only report the data of the performances of the patterns. The baseline was set from ms to ms before the onset of the tone. The noise power was estimated by the standard deviation in the baseline time interval, i. The SNR averaged across all participants was Time windows for statistical analyses of ERP data were chosen based on visual inspection of the grand average and centered around the maximum of the differences between correct and incorrect performed notes. For the behavioral data, we analyzed the MIDI velocities i.
The inter-onset intervals IOIs were calculated between the onset of an erroneous note and the onset of the previous note played by the same hand , between the onset of the simultaneously played correct note and the previous correct note played by the same hand , and between the onset of successive correct notes i. The signed values of the asynchronies of keypresses were calculated between errors and the simultaneous correct notes, and between two simultaneous correct notes. All behavioral data were statistically analyzed using repeated measures ANOVAs and paired samples t -tests. Pianists pressed incorrect and correct keys with different MIDI velocities.
This pattern of results indicates that the lower velocity of the erroneous keypress did not influence the simultaneous correct keypress of the other hand. Pianists produced correct and incorrect keypresses with different IOIs. Note that the overall tempo i. This is based on the fact that participants could choose their own fastest possible tempo whenever they were not able to perform in the instructed tempo, resulting in a slower mean performance speed. Figure 2. A shows the grand-averaged waveforms time-locked to the onset of keypresses.
Compared to correct keypresses, incorrect keypresses elicited an increased negativity before a wrong key was actually pressed down. The difference was maximal around ms before the onset of the keypresses and showed a central distribution see Figure 2. The pre-error negativity was followed by a later positive deflection with an amplitude maximum at around ms after the onset of an incorrect note. This potential showed a fronto-central scalp topography see Figure 2.
A and 2. A Grand-average ERPs elicited by correctly and incorrectly performed keypresses.
Sources of Errors
The arrow indicates the note onset and thus the onset of the auditory feedback. The grey areas show the time windows chosen for statistical analyses for electrodes that were included in the ROIs. Error awareness appears to be related to the posterror changes in the response speed, but the results are still variable. Thus, it appears that our finding that the uncorrected errors were followed by a slower response speed as compared to the corrected errors does not contradict the findings of any of the above three studies.
In addition, corrective behavior imposed other PEAs in reducing the lapses omissions and enhancing the response speed, particularly after TSD. A cerebral mechanism might be involved in the effect of error correction on PEAs because EEG beta activity was increased after erroneous responses compared to after correct responses. By explicitly instructing the workers to correct their errors immediately when the errors are committed might help maintain PEA functions. Last but not the least, further studies are required to determine the precise effect of compensatory mechanisms that are induced by intentional error correction to counteract TSD on PEAs and the mechanism behind this effect.
The authors would like to thank the National Science Council of the Republic of China, Taiwan for financially supporting this research under Contract No. The authors would like to thank I. Cheng for her assistance on programming. Shulan Hsieh declares no conflict of interest. Cheng—Yin Tsai declares no conflict of interest. Volume 18 , Issue 2. The full text of this article hosted at iucr. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account.
If the address matches an existing account you will receive an email with instructions to retrieve your username. Journal of Sleep Research Volume 18, Issue 2. Tools Request permission Export citation Add to favorites Track citation. Share Give access Share full text access. Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article. Summary Previous behavioral and electrophysiologic evidence indicates that one night of total sleep deprivation TSD impairs error monitoring, including error detection, error correction, and posterror adjustments PEAs.
NS, normal sleep; TSD, total sleep deprivation. Thus, the mean value was calculated from the data of 15 participants. Data are depicted as the mean SD. Figure 1 Open in figure viewer PowerPoint. Figure 2 Open in figure viewer PowerPoint. Figure 3 Open in figure viewer PowerPoint. EEG power spectral activity Electroencephalogram power activity was significantly affected by the sleep conditions, response type correct response versus error or postcorrect response versus posterror response , and the interaction between the sleep conditions and response type at different frequency bands.
Acknowledgements The authors would like to thank the National Science Council of the Republic of China, Taiwan for financially supporting this research under Contract No. Conflict of interests Shulan Hsieh declares no conflict of interest. Beck, A. Google Scholar. Crossref Google Scholar. Citing Literature. Volume 18 , Issue 2 June Pages Figures References Related Information. Transponder availability and bandwidth constraints have limited this growth, because transponder capacity is determined by the selected modulation scheme and forward error correction FEC rate.
Error detection and correction codes are often used to improve the reliability of data storage media. The "Optimal Rectangular Code" used in group coded recording tapes not only detects but also corrects single-bit errors. Reed Solomon codes are used in compact discs to correct errors caused by scratches.
Error Correction and Detection Codes
Modern hard drives use CRC codes to detect and Reed—Solomon codes to correct minor errors in sector reads, and to recover data from sectors that have "gone bad" and store that data in the spare sectors. Filesystems such as ZFS or Btrfs , as well as some RAID implementations, support data scrubbing and resilvering [ citation needed ] , which allows bad blocks to be detected and hopefully recovered before they are used.
The recovered data may be re-written to exactly the same physical location, to spare blocks elsewhere on the same piece of hardware, or the data may be rewritten onto replacement hardware. DRAM memory may provide stronger protection against soft errors by relying on error correcting codes. Error-correcting memory controllers traditionally use Hamming codes , although some use triple modular redundancy.
Interleaving allows distributing the effect of a single cosmic ray potentially upsetting multiple physically neighboring bits across multiple words by associating neighboring bits to different words. As long as a single event upset SEU does not exceed the error threshold e. In addition to hardware providing features required for ECC memory to operate, operating systems usually contain related reporting facilities that are used to provide notifications when soft errors are transparently recovered.
An increasing rate of soft errors might indicate that a DIMM module needs replacing, and such feedback information would not be easily available without the related reporting capabilities. One example is the Linux kernel 's EDAC subsystem previously known as bluesmoke , which collects the data from error-checking-enabled components inside a computer system; beside collecting and reporting back the events related to ECC memory, it also supports other checksumming errors, including those detected on the PCI bus.
A few systems also support memory scrubbing. From Wikipedia, the free encyclopedia.
Error detection and correction - Wikipedia
Not to be confused with error handling. This article is about computing. For knowledge, see fact checking and problem solving. Techniques that enable reliable delivery of digital data over unreliable communication channels. This article needs additional citations for verification.
Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Main article: Automatic repeat request. Main article: Hybrid ARQ. Main article: Parity bit. Main article: Checksum. Main article: Cyclic redundancy check.
Main article: Cryptographic hash function. Main articles: Forward error correction and Error correction code. Main article: ECC memory. Retrieved August 21, Retrieved 12 March Andrews et al. Fundamentals of Error-Correcting Codes. Cambridge University Press. Scott A. Retrieved Linux Magazine. Linux kernel documentation. Archived from the original on