**7. Latency of wavelets transform technique**

The real–time processing is possible using CWT and signal events estimator algorithm. The most important is the latency of the algorithm for the real–time applications like HCI. It is possible to use the alone EOG signal directly to control a computer. The blinking pulses are used as the additional control signals. The applications of the 3/4 electrode system need the low–latency processing, but the median filters are not suitable. The wavelets transform–based approach discussed previously has important advantage over other discussed techniques.

The latency of the system is limited by two factors. The first factor is the blinking pulses width. The saccades are immediate signals that are detected using tens of samples, depending on the sampling rate. They are well defined in time domain and occupies a very short time periods.

The long width of blink means that a system is limited only to the typical blinking. The typical blinking takes about 300–400 ms. It means that the length of the wavelet for the largest scale of CWT should be similar in a value or larger. CWT may process data in real–time and update results for every new samples. The forbidden region of detection occurs for the latest samples and is well shown in the right part of the CWT result e.g. (Fig. 11b). The calculation of the correct values in the cone that starts in the boundary samples is not possible. The calculation results (false) are available due to zero padding. The width of half–cone (Fig. 19) is the half of the wavelets width at the largest scale. It means that for typical blinking the latency is about 150–200 ms appropriately.

The tracking algorithm used for the selection of the peak lines is very fast due to the limited number of motion vectors and the high resolution of CWT.

16 Will-be-set-by-IN-TECH

<sup>0</sup> 0.02 0.04 0.06 0.08 0.1 <sup>0</sup>

<sup>0</sup> 0.02 0.04 0.06 0.08 0.1 <sup>0</sup>

**std** (c) Mean position error (in the number of samples) of detected

saccades

**std** (b) Saccades overdetected

The plots are similar to the previous Test 5. It means that there is no significant influence of

Additionally, in all tests there is no one wrong detection of the saccade slope direction (falling

The real–time processing is possible using CWT and signal events estimator algorithm. The most important is the latency of the algorithm for the real–time applications like HCI. It is possible to use the alone EOG signal directly to control a computer. The blinking pulses are used as the additional control signals. The applications of the 3/4 electrode system need the low–latency processing, but the median filters are not suitable. The wavelets transform–based approach discussed previously has important advantage over other discussed techniques. The latency of the system is limited by two factors. The first factor is the blinking pulses width. The saccades are immediate signals that are detected using tens of samples, depending on the sampling rate. They are well defined in time domain and occupies a very short time periods. The long width of blink means that a system is limited only to the typical blinking. The typical blinking takes about 300–400 ms. It means that the length of the wavelet for the largest scale of CWT should be similar in a value or larger. CWT may process data in real–time and update results for every new samples. The forbidden region of detection occurs for the latest samples and is well shown in the right part of the CWT result e.g. (Fig. 11b). The calculation of the correct values in the cone that starts in the boundary samples is not possible. The calculation results (false) are available due to zero padding. The width of half–cone (Fig. 19) is the half of the wavelets width at the largest scale. It means that for typical blinking the latency is about

The tracking algorithm used for the selection of the peak lines is very fast due to the limited

In this test the influence of smooth pursuit on the detection of saccades is tested (Fig. 18).

**6.7 Test 6 – two saccades, smooth pursuit**

<sup>0</sup> 0.02 0.04 0.06 0.08 0.1 <sup>20</sup>

**std** (a) Saccades missed

Fig. 18. Monte Carlo performance Test 6

**7. Latency of wavelets transform technique**

number of motion vectors and the high resolution of CWT.

smooth pursuit.

150–200 ms appropriately.

or rising).

Fig. 19. Latency area and latency time

### **8. Conclusions and further work**

The application of the EOG measurements system has been rising. The EOG systems are used in different and new application areas. The signal processing techniques for real–time processing with high accuracy and low–latency are necessary. There are many processing techniques for the detection and separation of blinking and EOG signals. Most of them are not suitable for more specific cases and new ones are necessary. The recent research shows the importance of the wavelets transform with carefully selected wavelets function.

In this chapter the new wavelets–based technique for the estimation of blinking and saccade time moments using CWT was proposed. The estimation of the blink and EOG signals is important for the real–time HCI systems. Previous work related to the optimization approach [Krupi ´nski & Mazurek (2010b;d;e; 2011)], using the blinking and eye movement model, was not well fitted for the real–time processing. The computation requirements were high and not defined by the number of processing steps due to the applications of the random number generator in the optimization algorithm. The proposed techniques in those papers were based on the evolutionary approach. The reduction of computation time was obtained by the selection of more efficient evolutionary operators. The computation time was also reduced by the reduction of the number of processed samples. The blink and saccade positions were also considered as a starting point for the optimization process near the global minima [Krupi ´nski & Mazurek (2010b)] and the sensitivity of this approach was considered in [Krupi ´nski & Mazurek (2010e)]. Additionally, the estimation of the smooth pursuit movements was observed to improve the results [Krupi ´nski & Mazurek (2010d)].

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The proposed in this chapter technique for the EOG measurements was based on the basic idea of CWT analysis introduced in [Mallat (1999)]. The application of the spatio–temporal tracking of singularity improved the detection of the time moments and types of singularities like blinks and saccades. Wavelet techniques have well defined computation cost that is also constant, what is important advantage of this technique. The analysis of the slope of singularity in CWT image at all levels allowed the detection of a specific feature.

The application of the peak tracking algorithm allowed the detection of such event. The computation cost was quite low and nowadays available processing devices allow the processing signal at low cost. The EOG signal sampling frequency is usually small (100–400 Hz typically) so modern microcontrollers have enough computation power for signal processing, recommended are DSPs (Digital Signal Processors).

In the chapter the signal performance analysis of the algorithm and latency behavior due to wavelets and tracking algorithm were considered. The latency was obtained from the analysis of the blinking pulses and it was possible to reach 200 ms latency for a signal processing part, and if there were no additional latencies related to the measurement system it was also the overall system latency.

Future research will be related to the implementation of the proposed approach using DSP.

The reduction of latency using the pattern recognition techniques is possible and will be considered in further research.

The synthetic, generated signals may fill the extended range of possible cases, which is important for the estimation of algorithm performance and finding the incorrectly estimated cases. The knowledge about incorrect cases is the source of important information for researches and developers about the estimation algorithm and further improvements are possible by the analysis of such cases.

### **9. Acknowledgments**

This work is supported by the UE EFRR ZPORR project Z/2.32/I/1.3.1/267/05 "Szczecin University of Technology – Research and Education Center of Modern Multimedia Technologies" (Poland).

### **10. References**


18 Will-be-set-by-IN-TECH

The proposed in this chapter technique for the EOG measurements was based on the basic idea of CWT analysis introduced in [Mallat (1999)]. The application of the spatio–temporal tracking of singularity improved the detection of the time moments and types of singularities like blinks and saccades. Wavelet techniques have well defined computation cost that is also constant, what is important advantage of this technique. The analysis of the slope of

The application of the peak tracking algorithm allowed the detection of such event. The computation cost was quite low and nowadays available processing devices allow the processing signal at low cost. The EOG signal sampling frequency is usually small (100–400 Hz typically) so modern microcontrollers have enough computation power for signal

In the chapter the signal performance analysis of the algorithm and latency behavior due to wavelets and tracking algorithm were considered. The latency was obtained from the analysis of the blinking pulses and it was possible to reach 200 ms latency for a signal processing part, and if there were no additional latencies related to the measurement system it was also the

Future research will be related to the implementation of the proposed approach using DSP. The reduction of latency using the pattern recognition techniques is possible and will be

The synthetic, generated signals may fill the extended range of possible cases, which is important for the estimation of algorithm performance and finding the incorrectly estimated cases. The knowledge about incorrect cases is the source of important information for researches and developers about the estimation algorithm and further improvements are

This work is supported by the UE EFRR ZPORR project Z/2.32/I/1.3.1/267/05 "Szczecin University of Technology – Research and Education Center of Modern Multimedia

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