**3. Simulation Results**

**Figure 2** shows the original EEG signal, and the clinical test signal is an original alcoholic EEG (lead FP1) (http://kdd.ics.uci.edu/databases/eeg). The length of the clinical EEG signal is 50 s, and the sampling rate is 256 samples/s. **Figure 3** shows the received EEG signal with a BER of 10− <sup>7</sup> and a mean square error (MSE) of 9.76 × 10− 10. The MSE was defined as following:

$$MSE = \frac{\sum\_{i=1}^{N} \left(x\_i - y\_i\right)^2}{N} \tag{2}$$

*xi* : the original EEG signal.

*yi* : the received EEG signal.

*N*: the length of original EEG signal.

**Figure 2.** The original EEG signal.

H0 = 1, H<sup>1</sup> = 0.971960, H<sup>2</sup> = √

58 Electroencephalography

over a wireless EEG transmission system.

for multiuser wireless EEG transmission system.

*MSE* <sup>=</sup> <sup>∑</sup>*<sup>i</sup>*=1

**3. Simulation Results**

: the original EEG signal.

: the received EEG signal.

*N*: the length of original EEG signal.

with a BER of 10− <sup>7</sup>

following:

*xi*

*yi*

\_

Further, the 64 DFB output bit streams were inputted to a 64-point inverse fast Fourier transform (IFFT), and the 64 parallel LDPC adaptive modulation FBMC EEG bit streams were outputted. The 64 parallel LDPC adaptive modulation FBMC EEG bit streams were inputted to a parallel to serial converter, and the serial LDPC adaptive modulation FBMC EEG bit streams

Step 1: On the basis of the output information obtained from the object-component Petri Nets

Step 2: Select the appropriate modulation mode to satisfy the requirements for transmission

Step 5: If the measured SNR of the received signal exceeds the threshold SNR at which the required BER for EEG packets is achieved, then update the power weighting as power weighting = power weighting − 1/30; If power weighting ≥1/30, go to Step 4; otherwise, go to Step 7. Step 6: If the measured SNR of the received signal is less than the threshold SNR at which the required BER for EEG packets is achieved, then update the power weighting as power weighting = power weighting + 1/30; If power weighting ≤1, go to Step 4; otherwise, go to Step 8. Step 7: Change the modulation mode. If the modulation mode is not OQAM, go to Step 4. Step 8: Increase the modulation mode. If the modulation mode is not BPSK, go to Step 4.

The carrier sense multiple access with collision avoidance (CSMA/CA) technology were used

**Figure 2** shows the original EEG signal, and the clinical test signal is an original alcoholic EEG (lead FP1) (http://kdd.ics.uci.edu/databases/eeg). The length of the clinical EEG signal is 50 s, and the sampling rate is 256 samples/s. **Figure 3** shows the received EEG signal

and a mean square error (MSE) of 9.76 × 10− 10. The MSE was defined as

*<sup>N</sup>* (2)

*<sup>N</sup>* (*xi* − *yi* ) 2

\_\_\_\_\_\_\_\_\_\_

were output. The proposed power assignment algorithm is summarized below:

(OCPN) model, determine throughputs for EEG packets transmission.

Step 3: Assign the initial value of power weighting as 15/30 for EEG packets.

Step 4: Measure the received signal to noise ratio (SNR) for EEG packets.

2/2, H<sup>3</sup> = 0.235147 (1)

**Figure 3.** The received EEG signal with a BER of 10− <sup>7</sup> and an MSE of 9.76 × 10− 10.

The block length of 2000 and rate of 1/2 irregular LDPC encoder, BPSK, and 64-points IFFT were used in the simulation, and the coefficients of the tap-delay line multipath channel model were [1, 0.5, 0.25, 0.125]. The assumed coefficients of multipath channel estimation (ACMCE) were [0.99, 0.495, 0.2475, 0.12375]. **Figure 4** shows the received EEG signal with a BER of 2.5 × 10− <sup>6</sup> and an MSE of 5.24 × 10− <sup>5</sup> . **Figure 5** shows the received EEG signal with a BER of 2.5 × 10− <sup>5</sup> and an MSE of 1.32 × 10− <sup>2</sup> . There was no difference on the human vision. **Figure 6** shows the received EEG signal with a BER of 1.7 × 10− <sup>3</sup> and an MSE of 0.21. **Figure 7** shows the received EEG signal with a BER of 8.8 × 10− <sup>3</sup> and an MSE of 1044.10. There were obviously differences on the human vision.

Our BER performance results for the proposed wireless EEG transmission system are shown in **Figure 8**. The six signs "△," "□," "o," "☆," "\*," and "x" denote the ACMCE with no error using BPSK, ACMCE with no error using OQAM, ACMCE with 1% error using BPSK, ACMCE with 1% error using OQAM, ACMCE with 10% error using BPSK, and ACMCE with 10% error using OQAM, respectively. As shown in the figure, the BER of BPSK was smaller than that of OQAM. Furthermore, the BER of the ACMCE with 10% error was larger than that of the ACMCE with no error. The MSE performance results for the proposed wireless EEG

**Figure 4.** The received EEG signal with a BER of 2.5 × 10− <sup>6</sup> and an MSE of 5.24 × 10− <sup>5</sup> .

**Figure 5.** The received EEG signal with a BER of 2.5 × 10− <sup>5</sup> and an MSE of 1.32 × 10− <sup>2</sup> .

**Figure 6.** The received EEG signal with a BER of 1.7 × 10− <sup>3</sup> and an MSE of 0.21.

**Figure 7.** The received EEG signal with a BER of 8.8 × 10− <sup>3</sup> and an MSE of 1044.10.

**Figure 5.** The received EEG signal with a BER of 2.5 × 10− <sup>5</sup>

**Figure 4.** The received EEG signal with a BER of 2.5 × 10− <sup>6</sup>

60 Electroencephalography

**Figure 6.** The received EEG signal with a BER of 1.7 × 10− <sup>3</sup>

and an MSE of 1.32 × 10− <sup>2</sup>

and an MSE of 5.24 × 10− <sup>5</sup>

and an MSE of 0.21.

.

.

**Figure 8.** The BER performance results for the proposed wireless EEG transmission system.

transmission system are shown in **Figure 9**. The six signs "△," "□," "o," "☆," "\*," and "x" denote the ACMCE with no error using BPSK, ACMCE with no error using OQAM, ACMCE with 1% error using BPSK, ACMCE with 1% error using OQAM, ACMCE with 10% error using BPSK, and ACMCE with 10% error using OQAM, respectively. As shown in the figure, the MSE of BPSK was smaller than that of OQAM. Furthermore, the MSE of the ACMCE with 10% error was larger than that of the ACMCE with no error. The relations of transmission power and noise power are shown in **Figure 10**. The signal to noise ratio, Eb/No, was defined as following:

**Figure 9.** The MSE performance results for the proposed wireless EEG transmission system.

The transmission BER was 10− 7. The four signs "o," "x," "△," and "☆," denote the ACMCE with no error using BPSK, ACMCE with no error using OQAM, ACMCE with 15% error using BPSK, and ACMCE with 15% error using OQAM, respectively. When noise power was fixed, the transmission power of ACMCE with 15% error using OQAM was the highest, the transmission power of ACMCE with 15% error using BPSK was the second highest, the transmission power of ACMCE with no error using OQAM was the third highest, and the transmission power of ACMCE with no error using BPSK was the lowest. The relation of ratios of the saving transmission power and noise power were shown in **Figure 11**. The saving in transmission power was defined as following:

transmission system are shown in **Figure 9**. The six signs "△," "□," "o," "☆," "\*," and "x" denote the ACMCE with no error using BPSK, ACMCE with no error using OQAM, ACMCE with 1% error using BPSK, ACMCE with 1% error using OQAM, ACMCE with 10% error using BPSK, and ACMCE with 10% error using OQAM, respectively. As shown in the figure, the MSE of BPSK was smaller than that of OQAM. Furthermore, the MSE of the ACMCE with 10% error was larger than that of the ACMCE with no error. The relations of transmission power and noise power are shown in **Figure 10**. The signal to noise ratio, Eb/No, was defined

 : *transmission power*

 : *noise power*

<sup>=</sup> *transmission power* \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ *noise power* (3)

*No*

*Eb*

**Figure 9.** The MSE performance results for the proposed wireless EEG transmission system.

*No*

as following:

62 Electroencephalography

*<sup>E</sup>*\_\_\_*<sup>b</sup>*

$$\text{power} \quad \text{saving} \quad = (1 - E\_b)^\* \ 100\% \tag{4}$$

**Figure 10.** The relations of transmission power and noise power.

**Figure 11.** The relation of ratios of the saving transmission power and noise power.

The transmission BER was 10− 7. The four signs "o," "x," "△," and "☆," denote the ACMCE with no error using BPSK, ACMCE with no error and OQAM, ACMCE with 15% error and BPSK, using ACMCE with 15% error and OQAM, respectively. When noise power was fixed, the transmission power saving of ACMCE with 15% error using OQAM was the lowest, the transmission power saving of ACMCE with 15% error using BPSK was the second lowest, the transmission power saving of ACMCE with no error using OQAM was the third lowest, and the transmission power saving of ACMCE with no error using BPSK was the highest.

#### **4. Conclusion**

In this chapter, a new FBMC-based wireless EEG transmission technology was investigated. Advanced LDPC, OQAM and BPSK adaptive modulation, FBMC, wireless communication methods and a power assignment mechanism were integrated. Simulation results were obtained for a variety of received EEG signals, with a range of BER results. The BER performances of the proposed wireless EEG transmission architectures and the MSE performances of the proposed wireless EEG transmission architectures were demonstrated. The beneficial effects of the ACMCE errors were indicated. When the transmission BERs were 10− 7, the relationship between transmission power and noise power, and the relationship between the saving transmission power ratios and noise power were discussed. The simulation results confirm that the proposed transmission system is an excellent wireless EEG platform that provides the high speed and low power consumption benefits sought.
