**8. Conclusions and future prospects**

In this chapter, two deep-learning models named LSTM and GRNN were applied to be embedded into a BCI system for MI-EEG signal classification to identify two imagery movements such as imagining right-hand and left-hand actions. In the proposed BCI system, the Emotiv EPOC IC with tg.SAHARAbox system and g.SAHARA electrodes are used to capture MI-EEG signals on C3, Cz, and C4. In this chapter, we use the Daubechies wavelet to get feature values on db4 and db2 coefficients. The GRNN can make each recurrent unit to capture variable-length sequences adaptively. Modified from LSTM, the GRNN has gating units that modulate the flow of information inside the unit, but without having a separate memory cell. In the GRNN, the parameters at each level are shared through the whole network. From the experimental results, the GRNN can get better performance than other strategies. Additionally, the GRNN can always obtain better performance than the LSTM in the application to control an electric wheelchair.

[4] Wu S-L, Liu Y-T, Hsieh T-Y, Lin Y-Y, Chen C-Y, Chuang C-H, Lin C-T. Fuzzy integral with particle swarm optimization for a motor-imagery-based brain computer interface. IEEE Transactions on Fuzzy Systems. 2016;**2016**. DOI: 10.1109/TFUZZ.2016.2598362 [5] Lin J-S, Lo C-H. Mental commands recognition on motor imagery-based brain computer Interface. International Journal of Computing, Consumer and Control. 2016;**25**:18-25 [6] Chatterjee R, Bandyopadhyay T. EEG based motor imagery classification using SVM and MLP. In: Proceeding of International Conference on Computational Intelligence and

A Motor-Imagery BCI System Based on Deep Learning Networks and Its Applications

http://dx.doi.org/10.5772/intechopen.75009

27

[7] Xie X, Yu ZL, Lu H, Gu Z, Li Y.Motor imagery classification based on bilinear sub-manifold learning of symmetric positive-definite matrices. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2016;**25**:504-516. DOI: 10.1109/TNSRE.2016.2587939 [8] Chatterjeel R, Bandyopadhyal T, Sanyal DK. Effects of wavelets on quality of features in motor imagery EEG signal classification. In: Proceedings of IEEE WiSPNET 2016

[9] Jois K, Garg R, Singh V, Darji A. Comparative analysis of classification techniques for motor imagery based BCI. IEEE Workshop on Computational Intelligence: Theories,

Applications and Future Directions (WCI); 2015. DOI: 10.1109/WCI.2015.7495507 [10] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural net-

[11] Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013;**35**:1798-1828

[12] Graves A. Supervised Sequence Labelling with Recurrent Neural Networks. Studies in

[13] Graves A, Mohamed A-R, Hinton G. Speech recognition with deep recurrent neural networks. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal

[14] Li N, Chen J, Cao H, Zhang B, Natarajan P. Applications of recurrent neural network language model in offline handwriting recognition and word spotting. In: Proceeding of 14th International Conference on Frontiers in Handwriting Recognition; 2014. pp. 134-139

[15] Moghadam SM, Seyyedsalehi SA. Nonlinear analysis of video images using deep recurrent auto-associative neural networks for facial understanding. In: Proceedings of 3rd International Conference on Pattern Recognition and Image Analysis; 2017. pp. 20-25 [16] Petrosian A, Prokhorov D, Homan R, Dasheiff R, Wunsch DII. Recurrent neural network based prediction of epileptic seizures in intra-and extracranial EEG. Neurocomputing.

[17] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation. 1997;**9**:

Computational Intelligence. Cham Switzerland: Springer; 2012

Networks; 2016. pp. 84-89

Conference; 2016. pp. 1346-1350

works. Science. 2006;**313**(5786):504-507

Processing; May, 2013

2000;**30**(1):201-218

1735-1780


**2. Related works**

4 Wireless Mesh Networks – Efficient Link Scheduling, Channel Assignment and Network Planning Strategies

*e4*

*e6*

A major challenge in WMNs is to provide QoS support and fair rate allocation among data flows. Almost all of the routing and scheduling algorithms presented in the literature have one common weak point: when the MBS collects requests larger than the frame length from all the MSSs, these algorithms shrink link durations to fit in the frames. Scaling down the link durations may cause some drawbacks in guaranteeing the QoS requirements of voice and video traffic. Two schemes can be exploited to overcome this problem: 1) A call admission control mechanism can be deployed to avoid link duration shrinkage; 2) A new scheduling method may be proposed to schedule the packets received from the underlying network. In this chapter we focus on the second solution with respect to the first solution.

Scheduling in WMNs is divided in two categories: centralized and distributed scheduling. In centralized scheduling, there exists one MBS and the other stations (MSSs) relay packets of other stations to/from end points (in this chapter we call these end points as MTs, while MSSs assume to be fixed). The main purpose of this chapter is related to centralized

On the other hand, rapid growth of wireless networks has commenced challenging issues in co-deployment of various technologies including WiFi, WiMAX. While WiFi networks are very popular for providing data services to Internet users in LAN environments, WiMAX technology has been adopted for MAN networks to provide urban accessibility to hot spots or end users. These two technologies seem to be competitors; however, they can interwork to gain metro-networks performance, cost effectiveness and coverage area. This configuration can be used in TDMA-WMNs, however when the same frequency band is employed with different network elements (e.g., the U-NII frequency at 5GHz may be shared among IEEE 802.16d and IEEE 802.11a or IEEE 802.11n), more complex strategies are required for

In this chapter, with respect to the interoperability of WiFi and TDMA-WMNs networks, we develop a scheduling and admission control mechanism among data flows such that the QoS requirements of delay sensitive traffic types can be provisioned and elastic traffic types

scheduling and packet translation from one technology to another.

*e5*

*e3*

*3*

*6*

*e7*

*e8*

*5*

*1 2*

**Figure 1.** A TDMA-WMN with its conflicting links

scheduling and admission control.

get a fair duration of bandwidth.

*e2 e1*

> Centralized scheduling mechanism in WMNs has been investigated in [1], [2], [5]-[10], [32- 33]. Most of the research activities in this area are not suitable for TDMA mesh networks (e.g., IEEE 802.16d). They consider only primary conflicts in which the connections share a neighbor, while TDMA-WMN is faced with secondary conflicts where the transmitter and the receiver share a neighbor, which can hear both transmissions.

> The main algorithm in IEEE 802.16d finds a link ranking during a breath-first traversal of the routing tree. This algorithm has no idea for spatial reuse in the network. Spatial reuse in these networks has been investigated in [5], [7]-[10]. Ref. [9] uses Transmission-Tree Scheduling (TTS) algorithm that is based on graph coloring. This algorithm don't consider the protocol overhead of TDMA scheduling. While [10] uses the load-balancing algorithm to increase spatial reuse, [8] considers Bellman-Ford method for both spatial reuse and minimum TDMA delay. These schemes don't take into account the underlying network behavior which can affect scheduling of traffic flows of other MSSs. On the other hand, these algorithms shrink the link duration when the frame is short for scheduling the links.

> Application of intelligent scheduling methods in wireless mesh networks has been inspired by the fact that finding a schedule in TDMA scheduling is *NP*-complete [11]. Ref. [12] uses fuzzy hopfield neural network technique to solve the TDMA broadcast scheduling problem in wireless sensor networks. Artificial neural network with reinforcement learning has been introduced in [13] to schedule downlink traffic of wireless networks. A genetic algorithm approach is used in [2] to find the schedule related to each link in a WMN. Here again, their scheduling method merely considers the traffic flown on the links; however, how these links empty their queues has not been elaborated.

> None of the above research activities, consider neither the underling network behavior nor the types of traffic streams flown on the links. Our system model is different from the

previous works in two aspects. First, we take into account the underlying network traffic related to each MSS. Second, the algorithm proposed in this chapter is such that shrinking the link duration doesn't affect the minimum QoS requirements of real-time traffic types.

Application of Genetic Algorithms in Scheduling of TDMA-WMNs 7

The control sub-frame is divided into MSH-CTRL-LEN transmission opportunities indicated in the ND (Network Descriptor). Each transmission opportunity comprises 7 OFDM symbols, so the length of the control sub-frame is fixed and equal to 7×MSH-CTRL-LEN

Nodes can transmit based on the granted bandwidth and a transmission schedule which is worked out using a common distributed algorithm. The data sub-frame is used for this purpose which is divided into transmission opportunities comprising 256 mini-slots based on the standard. However, there may be fewer than 256 mini-slots depending on the frame size and the size of the control sub-frame. Frame duration which is indicated in ND is determined by MBS to avoid losing synchronization with the connecting nodes. MSH-CSCH-DATA-FRACTION indicated in ND specifies the fraction of data sub-frame which can be used for centralized scheduling. The remaining part of the data sub-frame is used for

Providing QoS in IEEE 802.11e comes with a new coordination function called HCF. The HCF controlled channel access is for the parameterized QoS, which provides the QoS based on the contract between the AP and the corresponding QSTA(s). First, a traffic stream is established between the AP and an QSTA. A set of traffic characteristics and QoS requirement parameters are negotiated between the AP and QSTA and the traffic stream should be admitted by the AP. The QoS control field in the MAC frame format is a 16 bits field which facilitates the description of QoS requirements of application flows. Its TID (4 bits) identifies the TC (0-7) or the TS (8-15) to which the corresponding MSDU in the FB field belongs. The last eight bits are used usually by QAP to receive the queue size of QSTAs. After admission, the AP specifies the TXOP duration for the QSTA based on the traffic characteristics. So, the

Unlike WiFi, the QoS in the mesh mode of IEEE 802.16 is provided in a packet by packet basis. Each transmitted packet contains the mesh CID. Figure 5 shows the structure of mesh CID used in unicast messages. In order to enable differentiated handling of packets, the queuing and forwarding mechanisms deployed at individual nodes may make use of the values for the *Type, Reliability, Priority/Class*, and *Drop Precedence* fields. The *Type* field is used to distinguish between different categories of messages. This field may be used to

**3.3. QoS Comparison between WiFi and WiMAX mesh mode**

QoS is provided based on connections established between AP and QSTA(s).

OFDM symbols.

**Figure 3.** MAC PDU format

decentralized scheduling.

**Figure 4.** Frame structure for the mesh mode


**Author details**

Shama Kakkar, Runjhun Tandon\* and Nitin Tandon School of Chemical Engineering and Physical Sciences, Lovely Professional University, Phagwara, Punjab, India

\*Address all correspondence to<EMAIL_ADDRESS>
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