TA_CSPNN.py

"""
Temporally Adaptive Common Spatial Patterns with Deep Convolutional Neural Networks (TA-CSPNN)
v1.0.1

MIT License
Copyright (c) 2019 Mahta Mousavi 

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

#Note1: 'image_data_format' should be 'channels_first'
#Note2: We tested this using Tensorflow 1.12.0


from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.layers import Conv2D, AveragePooling2D, Lambda
from tensorflow.keras.layers import DepthwiseConv2D, BatchNormalization
from tensorflow.keras.layers import Input, Flatten
from tensorflow.keras.constraints import max_norm
from tensorflow.keras import backend as K

K.set_image_data_format('channels_first')



def TA_CSPNN(nb_classes, Channels = 64, Timesamples = 90, 
             dropOut = 0.25,  timeKernelLen = 50, Ft = 11, Fs = 6):
    
    # Input shape is (trials, 1, number of channels, number of time samples)

    input_e      = Input(shape = (1, Channels, Timesamples))    
    convL1       = Conv2D(Ft, (1, timeKernelLen), padding = 'same',input_shape = (1, Channels, Timesamples), use_bias = False)(input_e)

    bNorm1       = BatchNormalization(axis = 1)(convL1)

    convL2       = DepthwiseConv2D((Channels, 1), use_bias = False, 
                                   depth_multiplier = Fs, depthwise_constraint = max_norm(1.))(bNorm1)  
    bNorm2       = BatchNormalization(axis = 1)(convL2)
    
    lambdaL      = Lambda(lambda x:x**2)(bNorm2)
    aPool        = AveragePooling2D((1, Timesamples))(lambdaL)

    dOutL       = Dropout(dropOut)(aPool)
        
    flatten      = Flatten(name = 'flatten')(dOutL)

    dense        = Dense(nb_classes, name = 'dense')(flatten)
    softmax      = Activation('softmax', name = 'softmax')(dense)
    
    return Model(inputs=input_e, outputs=softmax)