The Gabor filter acts as a bandpass filter and provides good time-frequency localization (Gabor, 1946). Furthermore, using a bank of Gabor filters for the decomposition of signal makes it easier to extract discriminating information from a particular frequency range. The mathematical representation of the Gabor filter is as follows:

where f c - central frequency,

σ – Standard deviation of the Gaussian function.

The response z t is computed through the convolution of the input signal y t with the Gabor function g t as described by Equation (1). Finally, the magnitude of the Gabor response will be determined for feature extraction.

The feature extraction process is crucial in the classification process, as the choice of features significantly impacts the performance of the classification. Local binary pattern (LBP)-based histogram features are commonly used to extract features from the responses of Gabor filters (Kumar et al., 2015; Samiee et al., 2017; Sunil Kumar and Kanhangad, 2017; Kumar and Kanhangad, 2018). The length of the histogram (feature length) is 256. As we are conducting multichannel EEG signals (19 channels), extracting the traditional features will lead to a high-dimensional feature vector. Therefore, we have extracted four statistical features from each of the Gabor filter responses. In our study, statistical features such as entropy, standard deviation, skewness, and kurtosis were extracted from the magnitude of the response z t . Mathematical equations for the aforementioned features can be found in Sunil Kumar and Kanhangad (2017).

In this process, the feature vector is constructed through the concatenation of statistical features extracted from z t across all the channels. To classify the EEG segment into ADHD and HC, the feature vector is fed into classifiers. In our approach, we have used two classifiers; namely support vector machine (SVM) (Maniruzzaman et al., 2023) and k-nearest neighbors (k-NN) (Altınkaynak et al., 2020).

Feature selection (FS) is important for improving the performance of predictive models by eliminating redundant elements in a dataset, thereby maintaining only the most important features. In our study, we explored the t-test (Maniruzzaman et al., 2023) and the chi-square test (Rangarajan and Mahanand, 2014) to decrease the length of the feature vector and improve the accuracy (Acc) of classification.

The algorithm of our proposed approach is given below.

In our study, a publicly available dataset (Nasrabadi et al., 2020) has been utilized for detecting ADHD from EEG signals. The EEG signals acquired in this dataset include 61 children diagnosed with ADHD and 60 HC, and all the participants were within the age range of 7 to 12 years. The diagnostic criteria for the ADHD group with confirmation, are based on psychiatric evaluation in accordance with DSM-IV guidelines (American Psychiatric Association, 2013). Notably, the ADHD children had received Ritalin treatment for up to 6 months. The control group was free of psychiatric disorders, epilepsy, and high-risk behaviors. EEG recordings were acquired according to the 10-20 standard, utilizing 19 channels, and a sampling frequency of 128 Hz. In our study, the EEG signal is divided into segments with a duration of 30 and 60 s. For further information about the dataset, refer Nasrabadi et al. (2020).

To validate the performance of our approach, the following metrics are used namely, Acc, specificity (Sp), and sensitivity (Sn) in which Sp denotes the capacity to correctly categorize normal data, while Sn signifies the ability to identify ADHD-related events, whereas Acc is defined as the ratio of correctly classified segments to the total number of segments in the test set.

In order to study the influence of the number of Gabor filters, we have performed our experiments by selecting the number of Gabor filters as 3, 4, and 5. The central frequency ( f c ) and standard deviation ( σ ) of the individual filters are selected to cover the range of frequencies extending from 1 to 64 Hz (half of the sampling frequency). These parameters are shown in Table 1. Figure 2 shows an exemplary plot of the raw EEG signals and magnitude response of the Bank of Gabor filters with five filters (the parameters involved in each of these Gabor filters is shown in Figure 2).

To show the effectiveness of our approach, we have performed two sets of experiments: in the first set, we conducted 10-fold cross-validation, and in the second set, we divided the entire dataset into 80:20 train-test data.

The performance metrics obtained are shown in Tables 2–7. More specifically, Tables 2–4 show the results obtained when an EEG segment of 30 s is used, while Tables 5–7 show when an EEG segment of 60 s is used for classification purposes.

Tables 2–4, it is evident that our approach performs better when the number of Gabor filters is set to 3 or 5, while the performance of the approach was comparatively inferior when the number of Gabor filters is set to 4 for 30-s duration. It can also be observed from our results that the classification improved when FS was included at the same time, and the number of features performed was reduced.

To understand the impact of segment length on the performance of the proposed approach, we performed the experiments by segmenting the EEG signals for 60 s. These results are shown in Tables 5–7. It can be observed from Tables 5–7 that the proposed approach achieved a maximum Acc of 90.9% when a 60-s segment is used. The performance is inferior when compared to the performance achieved with 30 s of EEG data.

The performance comparison of our approach with existing approaches is shown in Table 8. Our approach has achieved better performance than the existing approaches in Chen et al. (2019), Altınkaynak et al. (2020), Ekhlasi et al. (2021), Kim et al. (2021), Parashar et al. (2021), Maniruzzaman et al. (2022), and Alim and Imtiaz (2023). The approaches in Chen et al. (2019), Altınkaynak et al. (2020), and Kim et al. (2021) have performed experiments on different datasets, while the approaches in Ekhlasi et al. (2021), Parashar et al. (2021), Maniruzzaman et al. (2022), and Alim and Imtiaz (2023) have performed experiments on the same dataset as ours. Chen et al. (2019) performed four distinct methods: relative spectral power, spectral power ratio, complexity analyses, and bicoherence for resting-state EEG feature extraction. The classifier constructed by selecting features from all four methods obtained an Acc of 85% on data acquired from 108 subjects. Kim et al. (2021) investigated the mismatch negativity (MMN) features, exploring both sensor-level attributes such as amplitude, latency, and source-level characteristics across various brain regions and achieved an Acc of 81%. It should be noted that authors have collected data from only 79 subjects. Altınkaynak et al. (2020) analyzed wavelet, non-linear (Higuchi algorithm), and morphological features (P300 latency and amplitude parameters) by using different classifiers and obtained the highest Acc of 91.3%. Parashar et al. (2021) used various combinations of channels from different brain regions (frontal, central, occipital, and parietal) that are directly fed to classifiers for classification purposes. When considering all channels of the right hemisphere, the authors reported an Acc of 84%. Alim and Imtiaz (2023) used EEG linear features from the four sub-bands and achieved an Acc of 94.2%. Ekhlasi et al. (2021) obtained the effective connectivity matrices (ECMs) of each individual by directed phase transfer entropy (dPTE) between each pair of electrodes, achieving an Acc of 89.7% with the selected features of the effective connectivity vector (ECV). Maniruzzaman et al. (2022) extracted morphological and time-domain features such as absolute amplitude, positive area, negative area, total area, peak-to-peak, mean, median, energy, power, standard deviation, skewness, kurtosis, coefficient of variation, H parameter activity, mobility, and complexity of EEG signals and obtained an Acc of 94.2%. However, our Gabor filter-based approach achieved the highest classification Acc of 96.4 %, outperforming the existing approaches. The superior performance of our approach is due to the efficacy of the Gabor filter in time-frequency domain localization (Gabor, 1946). Extracting features from the narrow-band signals (obtained after processing through the bank of Gabor filters) may lead to an effective time-frequency representation of EEG signals, which could be the possible reason for its superior performance. The key advantage of our approach is that it is simple yet effective for detecting ADHD. Whereas, the limitation of our approach is that the number of features increases as the number of Gabor filters increases.